{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年4月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "sys.path.append('../py')\n",
    "sys.path.append('../../py')\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import weighted\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "data=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and career='机器学习'  and monthly_salary<80000\", con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del data['publish_date']\n",
    "#del data['published_on_weekend']\n",
    "#del data['title']\n",
    "#del data['title']\n",
    "#del data['company_title']\n",
    "#del data['company_description']\n",
    "#del data['job_description']\n",
    "#del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_id</th>\n",
       "      <th>monthly_salary</th>\n",
       "      <th>headcount</th>\n",
       "      <th>title</th>\n",
       "      <th>zhinengleibie</th>\n",
       "      <th>career</th>\n",
       "      <th>real_province</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>company_description</th>\n",
       "      <th>...</th>\n",
       "      <th>bd_flink</th>\n",
       "      <th>bd_presto</th>\n",
       "      <th>bd_heron</th>\n",
       "      <th>ml_sklearn</th>\n",
       "      <th>ml_mllib</th>\n",
       "      <th>ml_tensorlayer</th>\n",
       "      <th>ml_paddlepaddle</th>\n",
       "      <th>ml_cntk</th>\n",
       "      <th>ml_theano</th>\n",
       "      <th>ml_mahout</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>120437646</td>\n",
       "      <td>29166</td>\n",
       "      <td>50</td>\n",
       "      <td>算法类：nlp/cv/语音/数据/量化-上深</td>\n",
       "      <td>算法工程师</td>\n",
       "      <td>机器学习</td>\n",
       "      <td>None</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>我们是世界29强平安集团旗下科技解决方案专家专注于为机构、企业、政府提供端对端智能科技服务基...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        job_id  monthly_salary  headcount                   title  \\\n",
       "861  120437646           29166         50  算法类：nlp/cv/语音/数据/量化-上深   \n",
       "\n",
       "    zhinengleibie career real_province province city  \\\n",
       "861         算法工程师   机器学习          None       广东   深圳   \n",
       "\n",
       "                                   company_description  ... bd_flink  \\\n",
       "861  我们是世界29强平安集团旗下科技解决方案专家专注于为机构、企业、政府提供端对端智能科技服务基...  ...    False   \n",
       "\n",
       "    bd_presto bd_heron ml_sklearn  ml_mllib  ml_tensorlayer  ml_paddlepaddle  \\\n",
       "861     False    False      False     False           False            False   \n",
       "\n",
       "     ml_cntk  ml_theano  ml_mahout  \n",
       "861    False      False      False  \n",
       "\n",
       "[1 rows x 122 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.sort_values(by='headcount', ascending=False)\n",
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pd_weighted_mean(group, avg_name, weight_name):\n",
    "    \"\"\" http://stackoverflow.com/questions/10951341/pandas-dataframe-aggregate-function-using-multiple-columns\n",
    "    In rare instance, we may not have weights, so just return the mean. Customize this if your business case\n",
    "    should return otherwise.\n",
    "    \"\"\"\n",
    "    d = group[avg_name]\n",
    "    w = group[weight_name]\n",
    "    try:\n",
    "        return (d * w).sum() / w.sum()\n",
    "    except ZeroDivisionError:\n",
    "        return d.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1077, 122)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3389"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7000., 14000., 20000., 30000., 50000.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_mean=weighted.weighted_mean(data.monthly_salary.values, data.headcount.values)\n",
    "q=weighted.weighted_quantile(data.monthly_salary.values,[0.025,0.25,0.5,0.75,0.975], data.headcount.values)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年4月中国机器学习工程师的平均工资为22763元，工资中位数为20000元，其中95%的人的工资位于7000到50000元之间。\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月中国机器学习工程师的平均工资为{salary_mean:.0f}元，工资中位数为{q[2]:.0f}元，其中95%的人的工资位于{q[0]:.0f}到{q[4]:.0f}元之间。')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2020, Developers in China earn 22763 Yuan as average, the median is 20000 Yuan, 95% of them earn between 7000 and 50000 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print(f'In {year}, Developers in China earn {salary_mean:.0f} Yuan as average, '\n",
    "      f'the median is {q[2]:.0f} Yuan, 95% of them earn between {q[0]:.0f} and {q[4]:.0f} Yuan.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Common Functions\n",
    "def get_sub_stats_by_col(data, col):\n",
    "    categories=data[col].unique()\n",
    "    salary_mean=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    salary_median=[]\n",
    "    \n",
    "\n",
    "    count=[]\n",
    "    \n",
    "    categorys_out=[]\n",
    "    for category in categories:\n",
    "        #print(feature)\n",
    "        idata=data[data[col]==category]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(np.average(values, weights=weights))\n",
    "        \n",
    "\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        categorys_out.append(category)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[col]=[c for c in categorys_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data\n",
    "\n",
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "    {\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f15f4a78_74f9_11ea_989a_701ce71031ef\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >career</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f15f4a78_74f9_11ea_989a_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_f15f4a78_74f9_11ea_989a_701ce71031efrow0_col0\" class=\"data row0 col0\" >机器学习</td>\n",
       "                        <td id=\"T_f15f4a78_74f9_11ea_989a_701ce71031efrow0_col1\" class=\"data row0 col1\" >22763</td>\n",
       "                        <td id=\"T_f15f4a78_74f9_11ea_989a_701ce71031efrow0_col2\" class=\"data row0 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_f15f4a78_74f9_11ea_989a_701ce71031efrow0_col3\" class=\"data row0 col3\" >20000</td>\n",
       "                        <td id=\"T_f15f4a78_74f9_11ea_989a_701ce71031efrow0_col4\" class=\"data row0 col4\" >50000.000000</td>\n",
       "                        <td id=\"T_f15f4a78_74f9_11ea_989a_701ce71031efrow0_col5\" class=\"data row0 col5\" >3389</td>\n",
       "                        <td id=\"T_f15f4a78_74f9_11ea_989a_701ce71031efrow0_col6\" class=\"data row0 col6\" >100.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15717be8cc8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career = get_sub_stats_by_col(data,'career')\n",
    "data_career.style.format({\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22763.224845087047"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data.monthly_salary * data.headcount) / data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3389"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1077.000000\n",
       "mean        3.146704\n",
       "std         3.470644\n",
       "min         1.000000\n",
       "25%         1.000000\n",
       "50%         2.000000\n",
       "75%         5.000000\n",
       "max        50.000000\n",
       "Name: headcount, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns=100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3389"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 城市"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f33750d0_74f9_11ea_a416_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >city</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow0_col0\" class=\"data row0 col0\" >北京</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow0_col1\" class=\"data row0 col1\" >26669</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow0_col2\" class=\"data row0 col2\" >7000</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow0_col3\" class=\"data row0 col3\" >25000</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow0_col4\" class=\"data row0 col4\" >52187</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow0_col5\" class=\"data row0 col5\" >717</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow0_col6\" class=\"data row0 col6\" >21.16%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow1_col0\" class=\"data row1 col0\" >上海</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow1_col1\" class=\"data row1 col1\" >26518</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow1_col2\" class=\"data row1 col2\" >7000</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow1_col3\" class=\"data row1 col3\" >25000</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow1_col4\" class=\"data row1 col4\" >58758</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow1_col5\" class=\"data row1 col5\" >809</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow1_col6\" class=\"data row1 col6\" >23.87%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow2_col0\" class=\"data row2 col0\" >杭州</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow2_col1\" class=\"data row2 col1\" >25558</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow2_col2\" class=\"data row2 col2\" >10008</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow2_col3\" class=\"data row2 col3\" >22500</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow2_col4\" class=\"data row2 col4\" >62469</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow2_col5\" class=\"data row2 col5\" >181</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow2_col6\" class=\"data row2 col6\" >5.34%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow3_col0\" class=\"data row3 col0\" >深圳</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow3_col1\" class=\"data row3 col1\" >22964</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow3_col2\" class=\"data row3 col2\" >5168</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow3_col3\" class=\"data row3 col3\" >22500</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow3_col4\" class=\"data row3 col4\" >54708</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow3_col5\" class=\"data row3 col5\" >481</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow3_col6\" class=\"data row3 col6\" >14.19%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow4_col0\" class=\"data row4 col0\" >广州</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow4_col1\" class=\"data row4 col1\" >20597</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow4_col2\" class=\"data row4 col2\" >9000</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow4_col3\" class=\"data row4 col3\" >17500</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow4_col4\" class=\"data row4 col4\" >40542</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow4_col5\" class=\"data row4 col5\" >367</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow4_col6\" class=\"data row4 col6\" >10.83%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow5_col0\" class=\"data row5 col0\" >成都</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow5_col1\" class=\"data row5 col1\" >18042</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow5_col2\" class=\"data row5 col2\" >10431</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow5_col3\" class=\"data row5 col3\" >12625</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow5_col4\" class=\"data row5 col4\" >41253</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow5_col5\" class=\"data row5 col5\" >103</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow5_col6\" class=\"data row5 col6\" >3.04%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow6_col0\" class=\"data row6 col0\" >南京</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow6_col1\" class=\"data row6 col1\" >17491</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow6_col2\" class=\"data row6 col2\" >8208</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow6_col3\" class=\"data row6 col3\" >17500</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow6_col4\" class=\"data row6 col4\" >37500</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow6_col5\" class=\"data row6 col5\" >204</td>\n",
       "                        <td id=\"T_f33750d0_74f9_11ea_a416_701ce71031efrow6_col6\" class=\"data row6 col6\" >6.02%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15717cfb888>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_col(data,'city')\n",
    "data_pl=data_pl[data_pl['head_count']>100]\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15550309825907338"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other=1-data_pl['percentage'].sum()\n",
    "other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "head_counts=list(data_pl['percentage'].values)\n",
    "head_counts.append(other)\n",
    "head_counts\n",
    "\n",
    "labels=list(data_pl['city'].values)\n",
    "labels.append('其他')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oz2ttR1GF4U7ntDLbGfJkDPD7+qbm24APtSxv3G47kLJDR4J+FY2cATyPFqBKE0HuTi2eYTtHnr0VeLm+qVl/DnxK9wn6TTRSCnwVd7FrfROkDumWknUNsZv9VoK93Q+8v2V54wbbQVT+6C9BP4lG5gMrgU+h//fqCC/IlG22M1j2BuCF+qbm99sOovJH9wn6gXve36dwR4AlltOoAnVXaol+b0A58JP6pualwL+3LG/sth1I5ZaOBrzOvcjt7biLC+svOdWvv6ROnW47QwG5Eni8vqnZz9PDvqAl6GXRyGJgFXCR7SiqsMUkvGEPNbW2cxSYucDK+qbmS2wHUbmjJehV0ciHgYeBybajqML3kkzeajtDgaoG/q++qfl/6puadfeRB2kJek00Moxo5Fbg/6HTnypDzakl+gv+2D4J3F/f1DzOdhCVXVqCXhKNzMQ9+vMy21FUcWlOLZlmO0MROAN4pr6p+SzbQVT26HmCXhGNnAf8HqixHUUVl7iEWk6I/bLedo4ikgKuB76l1yosfjoS9IJo5DrgL2gBqkFYIxM3285QZIK4lxn7c31Tc8R2GDU0WoLFLBoJEY38APg+es6nGqS/pE7V3wOD8xbc0ygm2g6iBk+/+YtVNFKDO/r7kO0oqrjd5Zw2xXaGInYi7kV7Z9kOogZHS7AYRSNTgMcAvUCoGpKEBDdtkdrxtnMUuTrgn/VNzafZDqIGTkuw2EQjc4FHcd+BKjUka6Vuk+0MHjECuK++qflC20HUwGgJFpNoZBnwIDDWdhTlDX9NnWI7gpdUAHfUNzW/23YQlTktwWIRjTQC96JHgKosutM5rd52Bo8JAb+sb2r+hO0gKjNagsUgGrkCuA13hXulsiIpga0bZWyd7RweZIBv1zc1f8N2EHV8WoKFLhr5GLACPQVCZdkGGd9iO4PHfaa+qfkX9U3NQdtBVP+0BAtZNHI98B3cd5ZKZdXdzmLHdgYfuBr3pHqdxSlQWoKFKhppAr5mO4byrjtSSyfZzuATFwH36OoyhUlLsBBFI58Gvm47hvKulJgdG2SCXmYrf04H7tIRYeHREiw00cgnca8Cr1TOvCbjXrWdwYdOB/5Y39Qcth3kWIwx/2aM8c2oVUuwkEQjHwf+x3YM5X33OguTtjP41L8AK+qbmvP+u9cYs8IYM90Yc7Ux5tpet48xxpxjjDndGHM6sBT4+sHP0/d5dlUhPeKwUEQjHwFusB1D+cPtqWV6aoQ9/wbsB67L8+smgGT6z96XgKoBTgbi6c9fSf85P/1nCbAb2JaHjHmnJVgIopH3AN+1HUP5Q0rM7jUyaartHD73ofqm5n0tyxv/M9cvZIwJikiqj9uHAz8HPiUiNxhjbgbqj3hYt4h4eik4nQ61LRq5EPcbUU+DUHmxSUavt51BAfCF+qbmj+fyBdL79u4zxtwH9C6zi4FbgV+IyMH9wyeKyNm9P3DXRPU0LUGbopFTgD+gI3KVR393FiZsZ1CHfLu+qfmqXG1cRFpF5BwRORf4a6+7VgMXiMhdvW7r66R+6eM2T9EStCUaOQFoBiptR1H+cntq6TjbGdQhBrixvqn5LXl+3XUicmTBvWSMecAY85wxZqMx5gHcfZeepiVoQzQyDvgbMMp2FOUvjph9L8iU6bZzqMMEgVvrm5rPydULGGPGAhP6uL33LNQq4Cbgk8CvgB8Bt+QqU6HQEsy3aKQad1qi3nIS5UNbZNRaMLr/ufCUArfXNzUvyuZGjTHnGWMeB5YDw9M3p4CDpzxca4z5vDHmc8DZwO96Pf0u4G3GmO8aYzzbFZ79wgpSNBLA3Rk9z3YU5U//cE6O2c6g+jUM+Gt9U/O0LG7zIeAsEbkKWI97usNDwGnp6c6LcEd9I4G3A4uBjwM7RKQT+FfcUyNKs5ipoJijp4VVzkQj/4M71aCUFZfEvvzKKpkx03YOdUwvAqe1LG/syPcLG2NGApOBVX3sM/QkLcF8cc8FXGE7hvIvRzgwLfbraiGgM0CF7/+AS1uWN+ov6BzTH4Z8iEaWAD+1HUP523ZGrtUCLBpvA75gO4Qf6A9ErkUjE4A/4+E5dVUc/pGa3207gxqQL9c3NV9kO4TXaQnmUjRSDtwGjLUdRanbU8tG286gBsQAv65vaj7RdhAv0xLMrR8CWT3kWanBEKHtaTnhBNs51IBV416Zvsp2EK/SEswV90CYq2zHUApgJ8NfcQj0tSyWKnwnoscU5IyWYC5EIw24o0ClCsKDqbmdtjOoIfm3+qbmf7cdwou0BLPN3Q/4e3RNUFVAbneW1drOoIbsO/VNzQtth/AaLcHs+3/AbNshlDpIhI4nnAY9Qb74lQJ/qG9qrrEdxEu0BLMpGnkncK3tGEr1tpvIKymCerkub5iCLrqRVVqC2RKNTAd+YjuGUkd62JnTbjuDyqqL65ua9c12lmgJZoO7MPYKQA9jVgXnttQyz18d3If+p76p+ahLI6mB0xLMjk8CS22HUOpIInQ/5pykJ1t7TwSdecoKLcGhikZmAf9lO4ZSfdnHsDUJQiW2c6icaKxvar7CdohipyU4FNFICHcaVNcFVQXpUeekVtsZVE59t76peYztEMVMS3BoPocui6YK2G2pZXo4vbeNQBfmGBItwcGKRuYD/2k7hpft6xbu3ZBkT5cz6G3s7HBIpPx5STYRYg87c3R/oPe9rb6p+e22QxQrLcHBiEaCwE1A2HYUr2jtES78TSfn/6qTS27tYmeHw5tv6eLJrSnOWdHF7s6+i/C1/Q6Nt3Rxxk2dfPJvPQB8/8k4i3/WQWdc+NuGJOGgyeeXUjAOULUmRkmZ7RwqL75f39Q80naIYqQlODgfAebbDuElv3khwSeWlHLPFZWMrTT8eGWCGy4o5fNnlnLBtBDPbO+7BD97Xw//eWYJ/7y6ki3tDg+0JHl2R4prTy7hqW0pKsP+LECAJ5yG/bYzqLwZjbtalRogLcGBikbGA1+2HcNrPrS4hPOmuYua7O4SLpgeZEldiIc2Jnlya4rTJvZ9AYS1ex0WjHPvG11haO0RRCDhwD0bklw4w78LpdyWWlptO4PKq3fqRXgHTktw4L4DDLMdwqse25xkf4+wpC6EiHDriwmGlxvC/XynXjorzJcfiHHnKwnu3pDijVNDnD8txF1rE9RVB7j4t13847Vkfr+IAiBC4kFnvu4P9J8f1zc1R2yHKCZaggMRjZwHXGY7hlft6xY+/NcefnFxOQDGGH7QWM7c0QHueKXvIvvCmaVcOCPEjasSXDkvTFWJ4R2zw0TPLqWmzNA4I8SfVify+WUUhDYq1nRTWmE7h8q78cANtkMUEy3BTEUjpcAPbMfwqnhKePsfuvj6G8uYXBPgGw/H+OVzcQAO9Ag1Zf3v25s/NsimVodPnPb6OeHr9jpMG24oDRkcHx4c+qRz4l7bGZQ119Q3NZ9iO0Sx0BLM3GeAGbZDeNXPn0nwzPYUX/tnjLNv7qS+JsCvnk9w5k2dpATOnxbk5d0pvnB/z1HP/dYjMT6xpISK9EEwbTFhbFWAWbVBfvp0nHOn+m+/4O2pZTpl72/ftB2gWBgRH75NHqhoZBKwBii3HUWp4xEhOTv2855OynVBd3+7qGV54122QxQ6HQlm5qtoAaoi0UH5K1qAClhe39Tc92HV6hAtweNxV4Z5l+0YSmVqpXPCbtsZVEE4CbjSdohCpyV4fN9E/51UEbkjtbTSdgZVML5S39Sss1jHoL/cjyUaOR84z3YMpTIlQupeZ+FM2zlUwZgAfNR2iEKmJdgf92rxeoSVKipdlK3toEJXilG9Nem6ov3TEuzfFcA82yGUGohVzvRdtjOoghMBvmA7RKHSEuxLNFICfMV2DKUG6g7nNN3/o/ryofqm5im2QxQiLcG+XQVMsh1CqYEQwflbavEJtnOoglSCe6qXOoKW4JGikRDQZDuGUgPVTcm6Vqr0SvKqP/9W39S8wHaIQqMleLR3AzptoIrO8zJth+0MqqAZYLntEIVGS7A394rx19uOodRg3JVaUmo7gyp459U3NesFwXvREjzcO9BFslUREkH+kjpFv3dVJj5uO0Ah0RI8KBoxwOdtx1BqMGKEN+wjoueCqUxcXt/UPNZ2iEKhJfi6twGzbIdQajBelCnbbGdQRaMEuM52iEKhJfi6T9kOoNRgNadODdvOoIrKB+ubmstshygEWoIA0chiYIntGEoNVnNqyXTbGVRRGYW7KpbvaQm6PmI7gFKDFZPQa7sYXms7hyo6H7MdoBBoCUYjY4HLbMdQarBelsmbbWcoJsk2vdxi2qz6pubzbYewLWQ7QAH4IO6OYqWK0l9SSzz5c+zEOtl9+zdBHEy4lNq3fBanp4Pdt32dse/q/wIvybZd7LnrBjABwsPHMeKC/+DAQyuI79jA6Mu+Qs+m56ma/cY8fiUF7ePAPbZD2OTvkaC7UPYHbMdQaijuSi2ZajtDLnS+9ADVi9/KmHf8F8HK4XS+/BB7mr+DJGLHfF77s3cz8vzrGPtv/02ybQ+J3S04XW2UjJ1GfOcGQtU6c9zLBfVNzQ22Q9jk7xJ0p0H1fBlVtOIS3LidkZ78Hh62oJHyKScD4HS1EqoZQ+1bPospOfaFMoaf+R7Coya6z+tpI1BejYiA4xDb/BKlE+fkPHsRMfh836DfS1DPlVFF7RWZuMl2hlyLbV2NE+ugbOJsAqWVGT+vc/VDhEdOJjRsJCW1k0m27gJj2HnLZ0ns0d2ovVzh54vu+rcEo5ET0dMiVJH7a+pUT/8Mp7rb2XffTxh54cAGK4kDO2h78s+MOPd9AFQvfiuVs8/BhEupOGEpXRueykXcYlWOe2yEL3n6B+g4rrIdQKmhutM5rd52hlyRVII9t3+dmjOvJBQZnfHzUj0d7Lnjm4z8l48eNnJ0ejoJlJRjgmHAyUHionaV7QC2+LME3atF6ImiqqglJLhls4yeYDtHrnQ8fy/xHRtofexWdtzSROfqh456TPfG52h7+s7Dbmt7/A+k2naz794fs+OWJno2vUBi31ZKRk+hZNwJtD19J2W6X/BI0+ubmhfZDmGDERHbGfIvGrkQ+IvtGEoNxWpn0sMXxpefbjuH8oxvtyxv9N3ykf4cCfp46K+84+7UYtsRlLe8o76p2dgOkW/+K8FoZDjwFtsxlBqqO5zTJtnOoDylDvDdzIL/ShAuB/QK3KqoJSWw/TUZryWosu1y2wHyzY8l+C7bAZQaqldl3Ku2MyhPurS+qTloO0Q++asEo5EJwFLbMZQaqnucRXqMv8qF0YCvFlb1VwnCv+IuE6RUUbsjtbTOdgblWb6aEvVVCSbgEtsZlBqqlJhda2XiFNs5lGddUt/U7Jsr6/imBOesmDN6Qf3E+RdNGPfoLcOqHusyptN2JqUGY6OMXW87g/K0GuBC2yHyxTclCFyEMTUtJeGlXx814rRTJ9cFLqgb/8TPI9WPtAVMq+1wSmXqXmdB0nYG5Xm+mRL1zYoxc1bMuQO4qM87ReK1qdTzl7R3dr+rrX3WCMfx7YrqqvD9S+y/N7ws9dNs51Ce1gmMblne2GU7SK75ogTnrJhTAezBXS392ERSNY7zwps7OluvbG2fOTaV8uS12lRxcsTsmRr7zSjbOZQvvL1leeMfbYfItZDtAHlyHpkUIIAxwQPB4PxfR6r5dfUwqRJ58YKOrr1Xt7ZNnZxMTsxtTKWObbPUrge0BFU+vBHQEvSIwS2TZozpMGb2n6qr+FN1FeWOs+acru6dV7e21Z0YT+h0lMq7+5yFMdsZlG+8wXaAfPD8dOicFXMCwA6gNpvbLRF5dVlX96ZrWtvGzo/FT8zmtpXqz8Wx/1r3vEybYTuH8o0JLcsbt9kOkUt+GAnOIcsFCBA3Zuo/Kium/qOygpDI5sU9Pa9edaB9xGk9PbONnpCvcsARs+95mTrddg7lK+cAv7EdIpf8UII5XxU9aczEx8rLJz5WXk5AZPu8WGzdla3tw87u6p4bBF+tw6dyZ6uMXAfmVNs5lK9oCXpAXi8N4hgzblVZ2bhVZWUYkT0N8fiaK1rby87v7JpbAr5ZhUFl3z+ck3tsZ1C+4/n9gn7YJ7gJsH9Up0jrtETipXe2dQQu7uicVyaS2dGqSqW9LSKTMdMAACAASURBVBZd84ycoPufVb7Vtyxv3Gg7RK54ugTnrJgzCSi8/zyRzonJ5AuXtXU4l7Z3zKkSGWY7kipsIrROjf16mBDw0ypPqjBc3bK88WbbIXLF6z9Qy2wH6JMxlZvD4SXfHjl86WmT60rOnTj+qR/VVD98IBDYbzuaKkzbGfGKFqCyxNNTol7fJ5jX/YGDYkzpzlBo8Q+H1/DDmkhyhOOsuri9s/2KtvYTR6dSo23HU4XhwdS8btsZlG+dYztALnn9nWVhjgT7Y0xoXzB48s011We+ceL4UcsmTXj+qyOHP7g5FNpiO5qy67bUsqyf5qNUhurqm5o9e2qOZ0eCc1bMqcY9R7A4GRNoCwbn3lo9jFurh1HhOKvf2Nm185rW9snTEwm9lpyPiND+lJw403YO5WvnAJ68hJeXR4Kn4aGvrysQaLhzWNXZl9SNm7Joct36j48e9cALJSVrbedSubeLmlccAnq+qbLJs/sFPTsSpBj2Bw5SLBCYfl9lxfT73NVqNi7p7mm5urVt1Ck9sZNsZ1PZ909nboftDMr3zrAdIFc8M1LqQ3HtDxykpDGTH64oP+u948acNL9+4rarx45+6MHysucccGxnU9lxW2qZXt9S2Tahvql5uO0QueDJkeCcFXNCgO+Wl0oZM35ledn4leVlGJHdJ8Xia97T1l5xXmfXvJBH/6+9ToSux50GPUFeFYKTgIdth8g2r/5iPBmosB3CJjGm9sWy0trPlJViRA5MTyReendre7ixs3NuqVBmO5/KzF6q1yQJLbCdQyk8WoI5mQ41rspcbDtDiyy+dsERY2rWlZQs+1LtyFMWTZ6YfHPduMd+XT3ssU5jdF9TgXvEmd1mO4NSaZ485iArJWiMqTbG9F5pfApwSx+P+4wx5k1H3BY0xjyajRy9NGR5e95hTNXGcPi0b4wcftqSyXWh8yeOf/KnkeqHWwOm1XY0dbTbUss8uR9GFSVPluCQp0ONMaOBqcAwY8wJwP8CYaDKGHMf7rX13iUiOwABYunn/UNEzhGRlDEm26thaAlmwpiy7aHQKd8bUcP3hkcSo1LO02/t6Oh6V1v7iaNSjp6cbZkIPY84s3V/oCoUWoL9qAfeizv6Owe4VEQ6D95pjAkBqfSnkv4A6Oy1jWyv4q0lOFDGhPeEggtvrIlwY6TaiTjOc40dXQeubGs7YXwyNc52PD/az7A1ccLzbedQKm1MfVPzyJbljXttB8mmIZegiDxpjFkKjAZuA0YaY25P371XRN4BYIy5C3gAeLMxpjp921eAbw01Q2/plWImZHObvmNMoDUYnHdLZBi3VFdJpchL53d27b66tW3KlERysu14fvGYM0unqFWhmQ08aDtENmXrwJhLgEm4hVYGPCci5wKV4O73S99ugCeBt6eftxJ4f5YyHKTTR9lkjOkMBE7687Cqsy+uGz958eS6Vz5dO/LBl0vCnlxCqZDclloWsZ1BqSN4bko0G/sELwMeBcYBfwKGAUcedVgPPAOMAf6KW77vBpp5vRCzRadCc6gnEJh5d1XlzLurKgmLvLa0u3vT1QfaRy+MxfTfPYtEiD3kzNU3dKrQeK4EszES3Aj8AEBE7sQdibX0foCIbBCRzwCzgFdE5Hfp21MH/55FJ2R5e6ofCWOmPFhRcdZV48c0zK+fuOXasaMffLi87HnJ/j5e32mlck2MEj2fUxUaz5VgNvYJPmGMqcU9PbAU+Hfg8vTdpcaYU4AFwP3APhFJpO8zQ33tfkzL0XbVMaSMqXuivKzuifIyAiI758Tir1zZ2lZ1Tlf3XF2tZuCecBr0AsuqEHmuBLO1TzAMlAJNwG0icvD6dyuAK3B3pH4H+Fqv52yHQ/sLs7m6i15myDLHmDHPlZWe+YkxtQsW1E9se/v4sQ/fXlX5VBzitrMVi9tTy6ptZ1CqD6Pqm5o9dbFvI5K9mStjTEhEkv3cVyEiXVl7sX7MWTFnNzAq16+jBkGkbUoi+eI729oDb+nonFsu4uul7fojQmJW7KZEN6X676MK0TktyxsfsB0iW7K6bFp/BZi+Lx8FOAwtwMJlTPVrJeGlXxs1Yskpk+t4U934J34RGfZIuzG6NFgv7VSs0QJUBWy87QDZ5LV9NToVWiyMqdgaDp36nRHD+c7wmvjoVGrlJe2d3e9sa581wnF8femgp5yZnjoZWXmOp1aT8loJTrUdoBA5MYeuDV2Uji0lPCJ82H2p7hQIBCssXrjcmJJdodCinwyP8JOa6tRwx3n2oo7Otita208Ym0qNtRfMjjtSS6tsZ1DqGDy1T9BrJeir1UwkJaz99FrCtW6xjX/3eMomHn5UvSSFlhtaqDqpih2/20HdB+qI74yz8487mfzJyXSv72bYwmE24vfNmOD+YHD+LyPV/LJ6mAxz5MULOjv3Xt3aPm1SMllnO16uiZC6z1kw03YOpY5BR4IFbITtAPnUs7mHyJIIYy/rf7AU2xlj1IWjqJ5fTbAiSNfaLno29zDmsjF0r+9GUkIglJMrag2dMaY9aGb/sXoYf6weRrnjrH5DV/euqw+0TZyZSHhy1N9J2SudlM+ynUOpY9ASLGA1tgPkU9eGLtqfbadzdSeldaVMuGoCJnj46ZdlE8oom1BG98Zu2p5uY8I1E+jZ0oPEhc61nYz6l+I5jqg7EGhorqpsaK6qpMSRDad3d2++prVt3LxY3DMjp2ecGbttZ1DqODw1HVqgQ4BB81UJlk8pp/4z9Uz70jRIQfvz7f0+tv3ZdhAIlAWInBJh7317KRldwo7f7eDAYwfymDo74gEz7f7KirPfPX7szJPrJ276wJjaBx8vK32x2FerucNZWm47g1LHoSPBAuarBYfLJpYRCLvvY8qnlBPf0f+56KPfMpp9NfvY/9B+ahtrqftAHd2vdlNSW0LHyx3UnFa87x+Sxkx6tKJ80qMV5QREts+PxdZd2dpefVZX95wgWDziZ2BEcP6WWuSZUa3yLE+VoI4Ei9iWn26he1M34ghtz7RRNunopSZbn2hl1+27AHC6nENHgXa90kXFjApM0GBMrlawyz/HmHHPlJWd+dExtfNPrp+4//LxY/7ZXFmxMgGJ4z/brm5K17ZT6as3cqoo1dQ3NYeP/7Di4LUS9NUvkNFvGc2Wn25h/RfXUzGtgrLJZWz9xdbDHjNs4TB6NvXw6n+/Svdr3dQsq0EcwZQYQpEQXeu7KK0rtfQV5JYYM+ql0tIzmkaPWrSgfmLXJRPGPvKHYZVP9BjTbTtbX551pu20nUGpDHlmNJjVZdNsm7Nizmu4l21Sqn8inROTyRcub+tw3tbeMadKpCDOEflc4r1P/Db1xlNt51AqAye3LG981naIbPDaPkFfTYeqQTKmcnM4vORbI4fzrRE1sbGp1FP/2t4Ru7yt46QaxxluI5IIcndq8Qwbr63UIHhmJOiZEpyzYo4BdOV9NTDGlO4IhRb/YHgNP6iJJEc6zjMXt3d2XNHW1lCbcvL2g95Dyfr9VGsJqmKhJViAhuG9fZwqn4wJ7Q0GF9xUU81NkWFOteM8/y+dXfuvbG2bXpdMTcjlS78gU7YDWoKqWHjmXEEvlaCvDopROWZMoC0YnPu76mH8rnoYFY7z8rmdXbuuaW2rn5ZI1mf75e5MnVaS7W0qlUOemXXzUgnq/kCVM12BwKw7hlXNumNYFaWOs+7M7p6t1xxomzA7Hs/K6O0vqVOnZ2M7SuWJZ7rDM18IWoIqT2KBwIx7Kytm3FtZQVikZUl3z8arW9tqF/fEBrXmZ4+EN+wlMi3bOZXKoaJZhOJ4vFSCOh2q8i5hTP0/K8rr/1lRTlBk64Ke2IarWtsip3f3zAlkuI/6JanfCmgJqmLime7wzBcCHL1cilJ5lDJmwlPlZROeKi/DiOyeHYuveU9be8W5nV3zQsf4WWtOLfHSz6HyB898z3rmCwEc2wGUOkiMqX2hrLT202WlGJH9M+KJl9/d1h5u7OicVwKHLdHTrPsDVfHR6dACpCWoCpIYM3xtacmyL9aO5IujRrTXJ5LPXN7eziXtnXNDTnDXTkZMsZ1RqQHyTHd45gsBUrYDKG8wIk44STyUIh5OkQglSYRTJMPpP0uSpMJJSZYkSbl/J1WSxAkncUqSSElSnJIkEk4i6T8pSUI4CeEUhJPGhFPV5uFE9dMHpr9xy1smvrgNxDurmCvP65HwXmi0HSMrvFSCOhIsYAFHUqEU8XCSRLpcDpVKOEmyJEkynJTUoWJJkQoncUoSOCVJnJKkHCqVw4oldbBcxISSmHAKE3QwoRSBoEPw4J8Bh2DQIRgQggEhZIRQQAgbIYQQNhAGSoCwcQ9oKSPH+5l3j5r7bEdo0cSZ7EvtCrSdmcvXUiq7elbZTpAtWoJFLJiS5KHRSopEulQS4WS6WFIkQ26xOL1HLIc+EiLp0QuHiuVgqSQhlBJCKQLh1KFSCRz8M3iwVNxiCQWcQ8USMhBOF0uI14slCJSnP3yvp7RmxwsnvW9CMv7Sa42xuaN+U/bIZjEy0XYupTLkmd+3XirBrEyHhlKSCKWIhw4WyuulkgilSJakSB0csYSTpNIjlYPlIiVJcXqNWA4VSjiFhJOYUErMwVIJHV4ur49UXi+WYHq0EuxVLL1HLCG89X/oC44JJJ9cdP0uTGCuIbQxmezadW5iTtm94efrMOi0qCoGntn95JlfoB++PdU9abc8EkphQg6BoFsqr49Y0uXSexrMQNgIYdLFki6Ygx9K5cRzcz70SDJceRYAJhTc2b2xc1p4/pKxUvPQDnNAp0VVMdASLDRnvCwOsMx2DqWOZcv4Mx7fP6LhrEM3mFBoS9eLI6ZVz+eC+PxFvyp9sMUxUm8voVIZ8UwJeumqCz22Ayh1LJ0VY1vWznjHEUurhYK7ezZPF5FEmGDF+Yl57Yh39rcoz+qwHSBbvFSC3bYDKNWfVCDc/dTCz8Yx5rDV940JBVOSLE9KYj1AnTNyTp0z4iE7KZXK2AHbAbLFSyWoI0FVsJ5e8KmnnWDJCUffEwwDHIjv3H3wlvMS804LSmBD/tIpNWBaggWo3XYApfqyYcpF/+yoqju9zztNKASwrWvDof3zQQKlb4rPjyEk8xRRqYFqtR0gW7xUgvsAsR1Cqd4OVE9dvXHSBaf0/wh3JLi1a31d71vHyfBZk51RD+c2nVKDpiPBQtOwZnUKD/3HqOKXCFUcWDX/Y1UYU9rfY0x6JNie2DtJRPb2vu8NiTlLgxJYm+ucSg2CZ37XeqYE0/bYDqAUgIA8uej6tRIIHmcVmOChguxOtb962D0EShrjCwQhkZuUSg2almCB2n38hyiVe6tPvOKhWNnwY0yDppngoYUZdvds7jry7tESmTnVGfNIluMpNVS6T7BA6UhQWbd71Nxnd4w5te8DYY4SLDn4ty2d66r7esTZiVmnhyS4OjvplBoyB2izHSJbtASVyqKDC2NjTEYXHTUmECS9GPHO7temichRJ8oHCITeHF8QRohlOa5Sg9EWjUY9cxCilqBSWdJrYezaAT41DpCQeLUjyVf7esAoqZ4+IzXusSGHVGro9h7/IcXDayWo+wSVNemFsecO4qnxg39pTezd0d+Dzkg2nBGW4IuDCqdU9my0HSCbtASVyoKjFsYemEMluL2rz4EgAAFM8KL4okpElwhUVrXYDpBNXivBrbYDKP/pe2HsgTCHVobZ2rV27LEeOUKqpjSkJjw5+NdSash0JFjAdL1FlVf9LYw9MObQeYD74zunisgxj7xbmpx5RqmEnhv86yk1JC22A2ST10pwE+iJxSp/+l8YeyBM7+/ZQMzpOuabOYMJXBRfNByhc2ivq9Sg6EiwUKWXTvPUf5AqXMdcGHtAzGELZe/p2Xrcc7BqpHLSnNSkp4f+2koNWIvtANnkqRJM6//IAqWy5PgLYw/E4SW4tWtdRSbPOiU5/YwyCT+TnQxKZSSFx4698GIJ6n5BlVOZLIw9ICZwWAlu73p1akZPw5iL44vHIHoZMZU3W6LRqKcu8aUlqNQAZL4w9kAEDlslJuZ0jXQktSmTZ1ZL+YT5qfpns5dFqWPy3O4mL5agToeqnFl94nsezGhh7AEJpI68pT2xf0umz16UnHZGhZSszG4mpfrUYjtAtnmxBHUkqHJi16h5q3aMOeWMrG/YHF2CO7pfHdCU08WxxRMQ76zsrwrWy7YDZJtXS9Azi7uqwtBTWrPjxZOurct0YeyBMH2MBLd0rhs9kG1UUTZuUXKaLqmmcs1z56d6rgQb1qzuREeDKouGsDB2hoJHvWnbG9s6XUQGtDza/FT9siopeyJ7uZQ6iuf2P3uuBNP0sHGVNc/NuW6wC2NnxgSPunySIKG407NuoJu6OLZoKsK+7ART6jA7o9Fovwu8FyuvluAq2wGUN2wZf+bj+0ecONiFsTN0dAkC7Itt3z/QLVVQWrskOWPN0DMpdRTPTYWClqBS/XIXxr5sCAtjZ6if3YzbutYP6jzE2alJS6udcr32oMo2LcEiotOhakhSgZKupxZ+NjG0hbEzYwj1eSDX1q719YPd5kXxRScgemkxlVWe2x8IHi3BhjWrd+OxpX1Ufq1c8KlnnGDJjLy8WD8jwe5U+1hHnO2D2WQ5JSNPT56o58yqbNKRYJHR0aAalA1TLv5nZ9WELCyMnam+R4IAncnWQa/QcWJqwqk1TuUjg32+Ur30AJ7c1+zlEtT9gmrADkSmrd446fwsrwhzHCZk+rtrZ/fG2FA2/eb4wpOM4Lkj+lTevRSNRo86n9ULtASVSkuEKg6smvfRYVlbGDtDhv7Pv9/atXbEULZdRrjmzMSszUPZhlKAZ5fl83IJPm47gCoe6YWx10kgWJf3Fzfhfu/a1bNphogM6ULRM5xxi0c6w/45lG0o33vQdoBc8WwJNqxZvQOPzmGr7HvZXRh7sZUXN6F+fw4dSZUlJT7gk+aP1BhfMM8I24a6HeVbD9gOkCueLcG0B2wHUIVv16h5q3bmYmHsDBn6L0GAA/Fde4b6GiWEqs9JzB7UkabK99ZFo1HPfu9oCSpfy+XC2Bk7xoExANu61oey8TJTnTELRzvVD2VjW8pXHrAdIJe0BJVvuQtjfz6HC2Nn6tgjwa1d67O2n/LC+MkLjBg9UEYNxAO2A+SSp0uwYc3qncBq2zm8aHtiSMdqFITn5v7HI8lwRe4Wxs6UCR1zFNqe2DdJRIY8JQoQJlR1bmLOXkQvN6Yy9oDtALnk6RJM+4ftALbtSSZ596bDz7leF4vx3s2b+n3OtkSCKzdt5OrNm/jSju2ICDfs3sX7Nm9GRHiyqyvXsXNqy4QzH9s/fGaOF8bOjDnGgTEHdafaX8vW6012auePlRo9WlRlYl00GvX0AVV+KMEHbAewqTWV4vrt2+l2Xr9QgYjwjV07SUr/g4HfHzjAF8eM5aaJk9ieTLI2FmN/KkVDWSmrYzHGhbOym8qKjopxr62dftls2zled+yRIMCuns1ZfddxQXz+ooCYlmxu02s6OjpIpTx5fvhAePbUiIO0BD0uCHx7/HiqAq//V/9fWyunVFQe83kfq61lWql7znhrKsXwUBABUgIru7pYXF6Rw9S5kwqUdK1c+JkkxgyzneUQEzruO4qtnWuzupB3mGDF+Yl57Qh9Xsap2HR0dHDTTTcB0NbWxg033MDNN9/MzTffTGdn5zGf29zczCuvvALAk08+yc9+9jPi8TgbNmwgGLR3vFSBeMB2gFzzfAmmF9N+3nYOW6qCQYb1+kE+kEpxV1sbV4/IbCGSv7a1Ma2klNGhMDNKStmWTGAMXLF5ExtiQ1rRy4qVCz69Km8LY2fIZDAS3NHdMl1EslpYdc7IOXXOiKI/WrS7u5vbbruNeDwOwNatWznjjDO46qqruOqqq6is7P8N38aNG+no6GDmzJkA7Nixg5NPPplt27YRDve/iIGPeH53kudLMO0O2wEKxQ27d/GxUbWEzTGPygdgczzOTfv38bnRowG4csQILq6uptwEOK9qGA92duQ6blatn/qWhzqrxi+zneMoJnjckWBS4sNSktyQ7Zc+LzHvtKAEsr7dfAoEAlx66aWUpmcutmzZwqpVq/jJT37C3//+936fl0qluPPOO6mpqWHNGnddDRHBcRw2bNjAjBkF9V7JhpVe3x8I/inB220HKBRPdXVxw+5dXLlpI2tiMf53d9+XnGtNpfjU9m18dezYw0aS7SmHykCAEmM4xi7FgnMgMm31ponnnWo7R58ymA4FaEvsyfpC2EECpW+Kz48hJLO97XwpLS2lrKzs0OfTp0/nyiuv5H3vex9btmxh586dfT7vueeeo7a2lmXLlrF161aeeOIJpk2bxtq1a6murua3v/0tr72WteORitFttgPkgy9KsGHN6pXAFts5CsFfp05jxaTJrJg0mRNLS/lobS2Pd3bym/37D3vcjfv2sj2R4Gs7d3Llpo081dVFSzzOzNJS5pSV8ZsD+1lUURz7BROhyv02FsbOXCijebftXa8ef/g+CONk+KzJzqiHc7FtGyZOnEhpaSmBQICxY8eyd+/ePh+3Y8cOFi5cSFVVFXPnzqWlpYXZs2dz9tlnU1ZWxowZM1i92tdnWP3ZdoB88EUJpvl6SnTFpMn93rakspJ3DR9+2H2frB3NQ9NnHCrMxRUV1JeUMLOsjLqSEu6aMpV55eV5yT4UAvLk4uvXW1kYO0Mmg+lQgC1d68blKsMbEnOWBiWwNlfbz6df//rXtLe3k0gk2LBhA6PT0/lHGjFiBPvTb/62bdtGJBIBYO/evQwfPpxQKIQU03RHdq2NRqMv2w6RD34qQV8M7dXhXm648sFYaY2dhbEzltlI8EB851QRac1FgiCBksb4AkEo+lUQzjrrLFasWMGNN97IokWLGDVqFLt37+b+++8/7HEnn3wyLS0t3HTTTaxcuZKlS5cSi8WoqqqitraWp59+mqlTp1r6Kqzzze9L45d3OqtPbAgDu4GI7SwqP3aNmv/MiyddOx9jCvrNnkisPXbgBxmdsnHxxOueKQ9VLchVlvvDLz7wanDn2bnavioap0WjUV9cjq6gfzlkU8Oa1QngL7ZzqPzoKR2+/cWT3jup0AvQFSzJ9JF7Ylvbc5nk7MSs00MS9PWOMMV24AnbIfKlCH5BZJVvhvh+5phA4slF1+/BBEbZzpKZzEtwa9e6Y69yMEQBAqE3xxeEEYrvJFCVLbdHo1F/TBHivxL8K9BjO4TKrWfn/sejyXDFHNs5MmWMMZDZKQrbuzZMy3EcRkn19BmpcY/l+nVUwfLFUaEH+aoEG9asbsfnR4l63eYJZz12oEAWxh6geEYPcnqGpyTZkuMsnJFsOCMswRdz/Tqq4BzAB6vE9OarEkxbYTuAyo2OyvGvrZv+9gJaGHtAMj4qsz2xf2sugwAEMMGL4osqEbpz/VqqoPwuGo0W/RHCA+HHEvwb7o5f5SGpQEnXygWfLqyFsQfEZDQSBNjR/VpeLm0wQqqmNKQmPJmP11IF4xe2A+Sb70qwYc3qFPAb2zlUdq1cWHgLYw9Qxu++t3au7fvs7xxYmpx5RqmEnsvX6ymrXohGo0/ZDpFvvivBtJttB1DZs37qWx/qrCzAhbEHxGS8dufe2LbpIpKXqxobTOCi+KLhCMe+HpHyAt+NAsGnJdiwZvVLwErbOdTQ7Y9Mf3nTxHMLc2HsATEZjwQFCcWdnvW5TNNbjVROmpOa9HS+Xk9ZEQd+bTuEDb4swTQ9QKbIJUKV+5+d95Hqwl0YeyAyHwkC7Itt33/8R2XPKcnpZ5RJ+Jl8vqbKqzuj0ege2yFs8HMJ3kKGh6WrwlMMC2MPTGBAJbi1a13Z8R+VPQZjLo4vGo2Q0xVrlDW+nAoFH5dgw5rV+9BzBovWyw1XFcHC2ANgzICO+NzWtb4+R0n6VS0VdfNT9c/m+3VVzm3FPWrel3xbgmk/sB1ADdyu2pOf2Tl60Zm2c2TXwEaC3amOMY44eb/q96LktDMqpET3p3vLL6PRaF5OuylEvi7BhjWrHwD0nW0R6S4dsf3FWdcUycLYAxFwBvqMzuSBjblIcjwXxxZPQMjJJZ1U3gk+ngoFn5dg2v/aDqAy45hA4qnFnyuihbEHwAQHXII7uzda2addRdm4RcmpuqSaN9wRjUbzdqRxIdIShN8CO22HUMf37NwPP5oMFc/C2ANhCAx4OmpL19qRuciSifmpKcsqpUxXkyl+/2M7gG2+L8GGNatjwI9s51DHtnnC2Y8dGH5CMS6MnaGBjwR392yeLiLWjnB+S2zRFIR9tl5fDdnj0Wj0YdshbPN9Cab9CPT6aYXKXRj70mJdGDszJjjg67c5kipLStzaVFYFpbVLkjPW2Hp9NWTfsh2gEGgJAg1rVu/CnRZVBSYVKOlcueDTqeJdGDtTAy9BgP2xnVZPcJ6dmrS02inXaw8Wn/XoRcYBLcHevms7gDraUws/86wTLJluO0eumUEcGAOwrXtDKNtZBuqi+KITEHbbzqEG5IZoNDqo7zmv0RJMa1iz+jngfts51OvWT33rQ12V44p8YexMDa7LtnWum5jlIANWTsnIZcmZG2znUBnbg15E4BAtwcN91XYA5dofmfHyponnLrGdI29McFBPa0/unyjiWB+FNaTqltQ4lY/YzqEy8oNoNKoXS07TEuylYc3qfwD/tJ3D79yFsT9cjTEltrPkT2hQ+wQBulLtr2UzyWC9Ob7wJCPssJ1DHVM38H3bIQqJluDRvmI7gJ8JyBOLr9/gnYWxM2NMyAz2ubu6NxXEu/oywjVnJmZttp1DHdPP/Xq1iP5oCR6hYc3q+4BHbefwq5carn4wXlqzyHaOvDODP75la9e6SBaTDMkMZ9zikU6V7889K1BdwNdshyg0WoJ9+6LtAH60s3bB07tGL/TYwtiZGvxIcGd3y3QRKZgFkBvjC+caIe+Le6vj+n40GtXp2Ac0SQAAIABJREFU6iNoCfahYc3qvwP/sJ3DT7pLR2x/adbV9d5bGDtDJjTorzspiaqUJAvm6MwSQtXnJGZvt51DHaYN+IbtEIXIn79wMvMF2wH8otfC2NbWwrTNDGEkCNAa311Q699OdcYsHO1UP2Q7hzrkO9FoVJe460NBlaAx5gxjzJB+GWRLw5rVjwJ/sZ3DD7y8MHbGhjASBNjevaEgfm56uzB+8gIjRg+UsW838G3bIQpVQZUgUA/8yhhzrzHmviM+thpjZhhjzjXGfB3AGPM9Y8yUg082xvzdmCEcYXC06wFdVSGHvL8wdoaGcHQowNau9eOzFSVbwoSqzk3M2Ysw6NM/VFZ8JRqNttsOUagKqgRF5FfAl4Ak8B7cK7+/B/gVsCJ9ew+vL3ZdBZAuzQDQIyIDukL3saRXkbkxW9tTh/PFwtgZMoQGd7Z82oH4rikiUnAXup3s1M4fKzU6LWrPeuAntkMUsoIpQWPMRcaY/wE2AR/AXdbnZWAvcB6wFrf0LgdOM8ZcBMSBFBATEQdy8o7z88CBHGzX1/yzMHaGzNBKEDA9qc6CvDjqBfH5iwNiWmzn8Knro9FownaIQlYwJSgidwJPAcuAU4GPAaOArwPX4p7jsgV4BLcQVwElQE7/gxvWrN6DnkCfdX5ZGDtz4aGWIHtiWzqykSTbwgQrzk/Ma0d010KePRGNRv+QzQ0aYz5ljPlAr88/boy5+ojH/Lcx5sL036cYY36TzQzZZn0F+t5E5Nb0gTFfxi2+kcBooAE36wFgM7BXRLYYY8YA+fjB/z7wfuDEPLyW56UXxvbp+YD9MMEh/yxu7VxXObGyML9F65yRc+qcEQ9sCe4723YWn3CA67K5QWNMMzAFiKdn4kh/njLGvFFE3m2MCQIXAN81xrwVd9BS0CPRghkJAhhjSoFfisgXcachS3FL7nYROU9E7kk/NGyMGQVUikjOd/g2rFmdwB2ZqiHy3cLYGTJDnw5le/er00SkYA9COS8x77SgBArmfEaP+2E0Gn06mxsUkUbgp0AzsDz9cR9wg4i8O/2wy3B3Yxngnbi7qOqMMd8wxtyazTzZUlAlCFwBPGqMKQO+Cfwn7mkKl6ePDP1X3FHifOBM3P2FkIevo2HN6r/h/uerQfLnwtiZCg15JBh3eoY7pFqyECYnggRK3xSfH0MomNVtPGoHuT3PuQz3+Iwq3IEKcGgQ8ync4psLLMY9tqMa+CMwLoeZBq1gStAYEwY+AtwB/An4Hu4+wCTwPuBW4DXgrel3JG8GfpF+ekt6GB40uV1x5OMU+NC+UPl1YeyMmaGXIEB7Yl9BL1c2TobPmuyM0iu15NYno9FoLo8UbgSa0h/n9rr9FOD3QB1wCe5xG1fhjgyfhcJ881MwJYh7juCfcEd33xaRPwNhoFxE1uEeIfqsiLSnzwW8TUTuAhCRD6XXTrwplwEb1qxeh16BflB8uzB2xoLhbGxlR9drBX/wyRsSc5YGJbDWdg6P+ns0Gr0l2xs1xgR7LWSyXETOFpGzSV+WKT34eAT4Oe4R/l/EPXof3OM5bgSyOj2bLQVzYEy66L6c/vT+9G2PAY+l/76312OTuCPGI7fx+9wnJQr8KzA1D6/lCf5eGDszxmSnBLd0rR19Ys2p2dhUxnZ37qOmrJpwhsf2BAmUNMYXyB0lKxMYsvJ1K8AtnaweDNPLe4BLcac6jTHm0vTtJn3bO4Afkv593Uso/ZiPAyFjzMjev8sLQSGNBItCw5rVXfz/9u48PqryXvz455nJSljCEtZhDQEGFZDNdSwEl/IDrVr9Vbtda6s33rhVrb16e1/33Pu6bXPt66e1bX7m12tv3Wqv3lr3atWmggrKIqDBGdmFkACBEMg+2/P74wwlQIAkzJlzZs73/XrlFSbzzHm+KJnvnGf5PubKVccuQHAS1xfG7rGspCSDxs76yVrrtt6+7sG3HubtLSc/GP6+P1Xwladv49EVTwLwxNoXWPLkrbSF21m2fVWPE+ARw/WgqZPiI+TIsuR6yDCMz624sNb6t4lpqGVAjdZ6qdZ6KWbhkte01osT29yOiAGDgH3ATVrrRuBGzCkvR5E3pj5InED/a7vjcLq48kRWzXvwgJsLY/eY8iZlsZBGe8Pxjs29ec1HuzbQ0HqAyyZf1O3zb3y+jJiO8/K3HmNnUz3bG3excd8Wbpy5hA17QvTLzutTrAsi0y/K0t5gn14sjrcNC88KVErlKaVexExsD3Z56uvA2UqpJxKPc4A8rfVBzP3cfwbeUEq9AwQwq4A5iiTBvvsB5p5FcRLrZ965IpaVL2XReiQ5SRDgQGddjyscRWJRfvjmz/ANHMWfN3e/XmXlrvVcOW0hAJdMmMeq2k/RWhONRVm2fTULJ/Vtx4sHT9bS8Oxs9N/KIIq+u90wjA6rLq617gC+r7X+UdezK7XWHVrr24EfJh7Xaa1vSPz5rsTWtksTX9dprfdZFWNfSRLsI38o2Iy5gV50Y6dv4YqmwpKMKox9sMW6399kzQkC1LVt6fGt2Qs1b1IybDy3nXcj6+tD/HbtCye0aQu3M3JAEQCF+QPY39bIJRPn8c7WlYwaUMTNLzzAii8+7lOsw/TAySWxUcfPI4ne+Y1hGG9Y3YnWescpnnPUUV69IUnwDPhDwTcxC3uLLloKRm/bUvzVGXbGcLitkUdevguAWCxK1Rv/xMMv3cnK0MnfK/YfruMXr97HT/9wK2+sfRqAlz96nF+9/kO01myqW2912OHTNzm9urYtE3ratmbfZr4+8yqG9x/KtdMvY8XOE5NZQU4+HRHzZq0t3E5ca67yL+Kei77DwLz+lBZfwJ82LetzvIGoP5CtvTV9voC7bUUKeZwRSYJn7vuAnKKdYBbGvl+jVH+7YmjrbObpvz5EZ9QcHXp344uMLZrCPVf/gnXbltMR7n7dyLKal1gy7yYeuO7XBGvX0NzeREtHE2OHlVC7fzOD+w+3OvSkJMH2WMuIuI7v7knbCYVj2Nlkbi38ZM/njBk48oQ254ycyqraTwD4bN9WfIPMNtsP1jKhcAw53mziZ1CoxoPyXhmeW4Cmvc8XcacY8E3DMBxZMzZdSBI8Q/5Q8CBwm91xOIVZGDu72M4YlPJw86U/Ii+7AIDNdRuYXWyOzE4eNYOdDd0voCvIG0TdgW0cbmskGovQL6c/aE08HmNL/aeUjJppdehJK8TQGm3a2ZN2N8xYysqd6/jq727nqXUvsWTqAh5a/p/HtLmiJMAfN77Fv/7lV7wWqmZR8QU0d7ZSVDCEkqETeHbDqwTGzzmjeIfo/hP9sTGrzugi7vMTwzA+tDuIdOeYfYLpzB8Kvhyc5v81Lp8j3Fx8jSMKY+fnFBzzOBzpoLDfMAD65Q7gcPvBbl83few83q35IwdbG5gyehYej5dRQyayfe9nDO5fxM9fuZsbL7mHkYPHWxS5CoOmrTNM7cFDjBk8iILc3q+Xae7oZFfLlsj0wfNP27Z/bj+qrj72kJR5vnOOeTwgt4Dnv/4L3tu+mtvOu5GBueZN/iUT5wHw5+/8F8lwYXRqYJt374ZOFbX800YGWI2cbpMUcieYPHcDrp3XOFhYsnGXb5EjC2PnZucTiZkjjZ3Rdk5WY/rt9b/nWwt+yFXzv0skFiZUu5bSGdcxv+RScrJymTkxQM3OjyyMVEXbwhF+8/5qdjY28di7H9LS0f3CyedXb+CXf/mAdz4zd0O8v3kHP3/7fTqjUT7f08C+jm1DkhlZYd4ArvSXMry/dbtdFMpzZXjuYDStlnWSGdowh0GTdoC4m0kSTBJ/KNiOWUG91xuV0104u6Bx/Yw7Cp1aGHtcUQlb93wKwO4DWxk64MR5L4D9h/dwsGUfkWiYXfs3m3UugPZwK7nZ/cjyZmOe3WyZSH3TYa6aNZ1Lp5cwdcQwdjcdPqHRp7X1xDXcsegiDrS00dDcSl3TYc6bNJZdjYfIyfLS0FE7RWudlDnGVCrUBePOiY1zZHktB7nXMAwpO5ckkgSTyB8KBoE77I4jlTQqvmrug9u0xzvG7lhO5rwpV/D6mif5wwe/Ys/BnUwYPo3Pd69jWc1Lx7RbMvfvePTVe/nHp65lcEERU0efy96mXYwZOonxw6exrOYli+cFPZHi4UMZP3QwWxsOsLPxEOOHFp7Qauu+RmaONQvyTxlZxPb9jYAmFtds2tPAtJHDiRPLiehwrzbNO8X86ORAns7u256LzPe6YRhVdgeRSZSDjx9LW8Fp/meAb9gdRyrUTL/53X3D5yywO47TaWrdz7Y9Nfh9c8nPtW3h6il1HPxlCCLTtNa8+PFGDrV38M3zzyX7uKMGn1+9gYtLJjK6cCCf72lg98FDDCnox+odtUwfPZya2j0smj6Z7824ffmI/PG2z9H2xWHVVvt8zspBKAbYHYuD1APnGoaRtnvynEjuBK1RBqTlp/De2Fs0Z+2+otlp8SZbWDCM2cULHJsAAVAqan5TXDvnbEYNGsDGuhPf73KysojEzKId4WgUrWHWuNFcflYJ+dnZ+EcN55PaPdS1bUnb4tQDdT/frNgEyzdmppEIcL0kwOSTJGgBfyjYgllVPWPLQbXnDanbOP0mKYydVJ5YdXAra3bUAtAeiZCfc2Ie8w0exPaGRgDqmpoZXJAPwP7mVob274fX60FrTV3blnGpiz355kaLA/10zhq743CIuw3DOHmFc9Fn8gZmEX8ouA641+44rBBXnsiquQ82SmHsZPNEzy8ex9ovdlNZvZK41gzKz+ONT4/d13j2mBGs/WI3r6z/jA276vCPGk5HJMKAvFxGDBzAR9t2MWXEMFqiTWO0jjuuVmNvXNU5bwwaKw+ITQf/ZRjG/7U7iEwlc4IWC07z/yfm0UsZ4+NZdy/LtLqgTtDR9OvV6JZ5PWnbFo6waW8Dk4YNYWD+yUuFLvWVrSrIHnT6DYMOtt67/YM12du6P+Ii860GAoZhZOyokt3kTtB6/wAstzuIZMnEwthOoZSnx/sv+uVkM2vs6FMmQIB9HTvTvhTZrNjEiwp0nhuryewDrpUEaC1Jghbzh4IRzJPot9sdy5lqLhiz1e7C2JnNGzt9m96pbdt04h6LNPSVzrkT0TTaHUcKRYH/bRhGrd2BZDpJgingDwX3A1cBzXbH0lcxT07r2tk/wM7C2BlPeZM+N7G3/YvJXc9/S1f9yC06P1oSsjuOFPqBYRh9P5pD9JgkwRTxh4I1mHsHLS05YpXVc364we7C2JnPm/R/GzEdKYjp6NZkX9cOZ8fGXTgwnu+GswefMQzj53YH4RaSBFPIHwq+Cjxodxy9tbn42uVtBSMvtDuOzJf8O0GAQ+GGjNlbdmV47hQ0DXbHYaFq4Lt2B+EmkgRTzB8K/gfwlN1x9FRj4ZSNu3yljiyMnWmUBcOhAHXtWzPm9zyfnKEXRadmxJ1tN9YD1xiGkXY1X9NZxvxypJlbgHfsDuJ0wtkFjRtm3O7YwtiZx5qTzXa3bh5tyYVt4o/5zi+MF2TaxvHtwGLDME6smC4sJUnQBv5QMAxcAzh22bdZGPufHF0YO+Mo7+nb9MGhSMMErXWTJRe3ydLwnLOUZo/dcSRJA3CFYRiZ8vdJK5IEbZIorbYY2Gh3LN3ZOP07y8O5g+baHYerqCyrKleojlhrRg0h5pFdeElk+i6740iCVmCpYRgZX2vYqSQJ2sgfCjYClwM7bA7lGHuHz1mTLoWxM4kiS1l17f2dtS1WXdsuJfFR84bG+79vdxxnIApcZxiGY0eE3ECSoM38oWAdcBngiBV87XlD6jb6vzNJCmPbQFmXBGtbN2Xk/s4l4TkzlKbO7jj66LuGYbxpdxBuJ290DuAPBbcAV4C9hYLjyhteNffBgyg1xM443Mu6JLinfXuxzsBCwTlkDVwYObve7jj64H7DMNJmlXgmkyToEP5QcAOwFLCt1uO6mXeujGXln2VX/66nrFkdChCOdxTGie2wrAMbTYqPmDM8PjCd6vP+o2EYP7M7CGGSJOgg/lDwfczyam2p7nunb9GKQ4WTpTC2jZTKsmZ5aEJzuDFdhw1Pa3H43NlKq3RYKHOfYRj/YXcQ4ihJgg7jDwXfwVw1mrKFDGZh7Gtmpqo/cTLWDYcC1LdvS8uSfT2RTVb/SyPnHEDj5CHfuw3D+D92ByGOJUnQgfyh4HLMxTKWzxFGvbkta+f8AJQqsLovcRoq29Lfx91tm0dYeX27jY8XzRqpC504LKqBcsMwHrU7EHEiSYIO5Q8FPwRKgQNW9rNmzv2fxD1SGNsJFNYOhzZ21hdrrTNuq0RXV4RnzfNotcPuOLrQwG1yMrxzSRJ0MH8o+DGwEPNwzaTbXPzVZW39pDC2Y1g8J6jR3nC8fYuVfdgtG2+/yyMzm9GOOK1FA7cYhvH/7A5EnJwkQYfzh4KfAl+C5O6FMgtjL7wgmdcUZ0h5LU2CAAc662zdhpMKvvjQc3zxIXYPi8aBmw3D+I3NcYjTkCSYBvyhYAi4BPgiGdcLZ/c/sH7m7YOlMLbTZFueBHe3bs63ug8nuCwy8wKv9thVKq4D81T4J2zqX/SCJME04Q8FtwIXAB+fyXXMwtgP7kB5M+pkgUyglDfb6j7q2rdOtLoPJ/Diyf1yeFYnmliKu94PlBqG8UKK+xV9JEkwjfhDwXrMO8JX+3qNmuk3Lw/nDpqTvKhE8lg7JwjQEWstiutYrdX9OMEoPXj6+Piw91LY5WbgfMMwVqawT3GGJAmmGX8o2ApcDfyyt6/dO3zOmoaic6UwtlOpLOtKxnTREmnamYp+nKA0cs6FXu3ZlIKuPgAuMAwjo07rcANJgmnIHwrG/aHgncBd0LNVcO15Q3dLYWyn86ZkjnZv+45oKvpxAi+enCXh2RpNxMJungcuNQzD0u1MwhryhpjG/KHgLzAP5209VTuzMPYDTVIY29lSMScIUNu2aWgq+nGK4XrQ1EnxESssuvxDwA2GYXRYdH1hsZQMvwjr+EPBV4LT/F/CnCcc1V2bdbPu+jCWlS/DoI6XlZIkuL+jtkRr3amUyk1Ff06wIDL9op2e/cGoivmTdMkYcIdhGI8l6XrCJnInmAH8oeBa4DxgzfHP7fQtWnFoULEkwHSgUjMcGieeE9GdrjrJ3IMna2l4djaaziRcrgG4QhJgZpAkmCH8oeAu4GLg8SM/a+7vk8LYaSU1SRDgYOde181fDdMDJ5fERp3pys0PgHMNw/hLMmIS9pMkmEH8oWCnPxS8Bbgl6s09sGb2fUoKY6cPpTxeerjQ6UzVtW1xZaGEQNQfyNbemj6+/GFggWEYu5MZk7CXJMEM5A8FH183885LtSclU0wiucKp6GR325ZxqejHaTwo75XhuQXoXh1efRi4zjCMew3DcM3KWreQJJihvv3szeuB2cAf7I5F9EpKkmBrtGmM1vG9qejLaYbo/hP9sTGretj8E2CuVIDJXJIEM1h5Vemh8qrS64HbISkLAoT1rNzPdozW6OEdqerLaS6MTg3k6qwNp2n2JGYFGFctInIbSYIuUF5VWolZdzSjj9HJDCold4IA+zp2unZvm0J5rgzPHYzudo9tK+YRSDcZhtGbYVORhiQJukR5Vek6zOHRZ+2ORZyKStmc0+7WTYWp6suJCnXBuHNi49Ye9+OVwCzDMB7v7jUi80gSdJHyqtLm8qrSbwBXAa4oopx+UpcE93Z8UaK1TvUpC44yPzo5kKezP8Ychn4QCBiGISMmLiJJ0IXKq0pfBc4CHsM8/Vo4RuqSYExH+8V0xNVv+AqllobnHATmG4bxU8MwXP2hwI0kCbpUeVXp4fKq0n8AAkDI7njEESplC2MAmsIN+1LZn8NEgH8r1AWLDcNYb3cwwh6SBF2uvKr0A2AW8O+kcGWiOAnlSemdSH3bVre+B2wA5vsqAv/iqwjIv3sXc+svgOiivKq0s7yq9J8xF858ZHc87pbaJLi7bfPoVPbnAM3APcAcX0VA7v6EJEFxVHlVaQ1wIfB9TnM8k7CKJ6UVSQ5F9k/QWh9MZZ82eg6Y5qsIPOKrCMjcnwAkCYrjlFeVxsurSn8OnA382e54XEd5UlI7tGuPHbGWTD8NfRNwma8icIOvIlBndzDCWSQJim6VV5XuKK8q/TLwbaDe7njcQpHyJEhDR21LqvtMkXbgR8A5vorAO3YHI5xJkqA4pfKq0qeBycADQJPN4biAN+VJsLZt08BU92kxDTwD+H0VgR/7KgIpq8Ij0o/SWraJiZ6pLKsuBO4H7gL62RxORuo8/PT7OtZwcSr7zPbkHrpm3F0DlVIqlf1a5G3gfln0InpKkqDotcqy6pGYw0y3AnJeUxJ1Hn72PR3bE0h1v9eNv3er15NVnOp+k2g9ZvJ72+5ARHqR4VDRa+VVpXvKq0pvB6ZhDjulfAgvYymvLZ9KD0cOpOu87xfAt4DZkgBFX0gSFH1WXlW6rbyq9FvATOAVu+PJBAp7kmB9+7Z0GxLaDpQBU3wVgWd8FYF0i184hAyHiqSpLKu+APgJsMDmUNJWuOWlZfHIti+lut8hOaM2Xzbm2yWp7rcPNgE/BZ7xVQTklHdxxrLsDkBkjvKq0pXAwsqy6ssxK/Kn/M08/dnzK9kYri/WWrcopfrbEsDp1QA/Bp73VQRk+F0kjSRBkXTlVaVvAW9VllXPwDzV/hvIatKeUVl2Dc14OuPtW/K8/WbZ1P/JfARUAC/LkKewggyHCssltlbcDJQDk2wOx9EirW8vi4U/teUO+uIR1y4b06/ECXfvUeCPwCO+isCHdgcjMpskQZEylWXVHmAxcAdwOZAJ+9KSKtL21+WxznWX2NH3xP4zVs0vWjzfjr4TGoDHgcd8FYFdNsYhXESGQ0XKlFeVxoHXgdcry6qnYN4Z3gRkWsWSM5Bl2weD+vatE23q+kOgEvgfX0Wg06YYhEvJnaCwVWVZdX/M+qTlwHSbw7FdtH3l+9GOlSmtGNPV9RPu2+VR3rEp6KoOc4/pE76KQDAF/QnRLUmCwjEqy6oXAt8ErgEG2xyOLaIda1ZE25dfaFf/i8d8d8XAnGFW9d8BvAw8AbwtxxkJJ5AkKBynsqw6B3PO8AbgK4BTl+0nXbRj/YfR9urz7er/3CGLlk8ZNDeZc5IaWAE8DTznqwhIEXbhKJIEhaNVllXnA/8L+Frie4G9EVkr2lmzKtr2lm2LU4bnjfts4agbz3RYOgq8i7nC8yVfRSBdS7IJF5AkKNJGZVl1HrAIuBq4Ehhhb0TJFwuH1kZa/zTHrv4Vnsj1E+6LKaXyevnSduAt4EXgFV9FwC2n1Ys0J6tDRdooryrt4OjqUg9wPuZw6VeAqXbGljxZXjt718SzI/HOz3O8eWf3oPlmzKOL3sac42u1Njohkk/uBEVGqCyrHg1c3OVrBmBrQumLWGTHp5GWP55jZwwLRn5t2Yj8Cd1tmj8A/IWjSe+L1EYmRPJJEhQZqbKsegBwAUeT4nmkQem2eKT2s3DL87ZuFSkZOGfl7KGXXgDsxVzUsgL4K/CxlC4TmUaSoHCFyrLqLGA2R5PiRcBwW4PqRjxavznc/Hs7TnPowDyYdnX/rMEfLBl76xpfRWCrDXEIkVKSBIVrJarWzAVKgCldvg+yK6Z4tGF7uPlpKyu3aMyz+DZ2+aoBNt773GsRC/sVwpEkCQpxnMqy6iLMZNg1MZYAk7F4SDUeO7grfPi3yajYcgDYgZnwtgFBzGQXvPe51zJqAYtS6lygSGv91ina3Aj8SWt9KHWRiXQgSVCIHqosq1bAGI4mxSKgEPPO8WTfc3vTh44f3tN56PGRJ3m6GTO5NSa+H/lzA1AL7Dryde9zrzX3pt90opSaBSwB2jDvbM8DcoD3Ek08wNrE9yN3t1/DXCj1bOJxNvC51rouRWELh5IkKISFEnsbj0+OAzDfvONALPEVB2I63h7uPPRYHHPfXUfiqxlolOFKk1KqCPABnZj/3f4Zc2P+xkQTL2aVoYuA8EkukwO8pbWusTZa4XSSBIUQaUUpNRL47y4/Ogtzz+KRhBfVWl+aaPsEMOG4S7RrrRdbHKZIE7JZXgiRlrTWC5RSD2BWD+oHfENr/bBS6qMuzaZprY+pxXrc88LlJAkKIdJNB4BSaizmnN9VwGXARqWUj6PzgNB9wQQZ/hJ/I0lQWEIpNRMYoLV+3+5YRMYqwlwc8+XE41Zg1HFtNiql3sU8mqsQc7Ws1DUVfyNJUFilBXgUWHD8E0qp+4FPtNZvdvmZF3hPa23bWXoi7dwJjAOOrKYdxYk1ZNdhVrvZjflv8VPMRTFCAJIERRIppW7FPCU+mvhRfuJTOEAe8BOt9SuYw1Gdidf8VWu9UGsdU0q1pzpmkZZyALTWNwEopb6XePx44vH7ie8PAPMxz6UMJF77GvCMUmoOcI/WOp7SyIXjSBIUyVQIPKa1/t1p2mmOzsu0HvdzIU4n/7jH2zH3BKKUegSoV0oNA4YC12OeNvJ94A2tdatS6qvAfZh7OOWDl8vJFgmRNEqpe4EGrfVTp2jzGuaBqyOB5cCtwMfAz4AXjyxtF6IvlFJKH/emppQaCowH1h3/nBByJyiSycsp7uYS8355gAJWYX5KB1iDmQyFOCPdJTmt9ZHqOkKcwGN3ACKjDAVOVZtxAuZd3wjgM8wDckl8321pZEII0Q1JgiKZ5gOhkz2ptd6qtb4fmI5Zt/G/Ez+PHfmzEEKkkgyHiqRQSp0HDNJabzpNuylAo9b6yIZmZXlw4hhKqUeBJ4G7gLu01k2Jn38Psxbny8AiYDQwLPGyX2qt99oQrhCWkjtBkSwKcwXe6TwC/LjL43r423yh409+zxB5ie9PAH+nTC8Ad2OumnwTc5/nEuBXQCmw34Y4hbCc3AmKpNBaf9jDptdrrdu6vO7IHq8YIBvlLZAoIl3M0e0oxcAsoAlzm8ALmB9grsHcfD4ec1N5J+Z2hE8S/3+EyDiSBEUzspJoAAACB0lEQVRKdU2AImXiwC1a6xCAUqoc8/zBGmAm5h34lzCPJ5oF/B7z7g/gauCkh9UKke5kOFSIzKeB3ymlPlZKPY65EncScDNH53F3Jx7nAd/EPI0ezIT490qpXh0OLES6kCQoRObLB64FyjDn9jYAizEPnX1KKbUA82Daf8c8gf1lYAiA1noP5oG1159wVSEygCRBITLfOMzN4oOARmAH5jDoTwC01u8C/wrMxhz+nI152j1KqfMxF8ic7IR2IdKazAkKkcGUUgOBgcDFwEPAvwCPA+9hHkY7PNFmOzAPGItZ0u5/MFf8NgMPJxKlEBlH7gSFyGy3AU8BHwB/jznsWa21vg5zyPNRzPm/XKBCa70AqMD8gJyttd4oCVBkMimgLUQGU0plA2itI0opBYzUWtd3eX6K1nqTUsqD+X4gWyGEq0gSFEII4VoyHCqEEMK1JAkKIYRwLUmCQgghXEuSoBBCCNeSJCiEEMK1JAkKIYRwLUmCQgghXEuSoBBCCNeSJCiEEMK1JAkKIYRwLUmCQgghXEuSoBBCCNeSJCiEEMK1JAkKIYRwLUmCQgghXEuSoBBCCNeSJCiEEMK1JAkKIYRwLUmCQgghXEuSoBBCCNeSJCiEEMK1JAkKIYRwLUmCQgghXEuSoBBCCNeSJCiEEMK1JAkKIYRwLUmCQgghXEuSoBBCCNf6/79TULVGeS4uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(head_counts, labels=labels, autopct='%1.1f%%')\n",
    "plt.title(f'算法工程师去哪里')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f470e51e_74f9_11ea_926f_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >pl_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow0_col0\" class=\"data row0 col0\" >2</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow0_col1\" class=\"data row0 col1\" >r</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow0_col2\" class=\"data row0 col2\" >28016</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow0_col3\" class=\"data row0 col3\" >27500</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow0_col4\" class=\"data row0 col4\" >12106</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow0_col5\" class=\"data row0 col5\" >58333</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow0_col6\" class=\"data row0 col6\" >179</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow0_col7\" class=\"data row0 col7\" >3.03%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow1_col0\" class=\"data row1 col0\" >3</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow1_col1\" class=\"data row1 col1\" >go</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow1_col2\" class=\"data row1 col2\" >27375</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow1_col3\" class=\"data row1 col3\" >25000</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow1_col4\" class=\"data row1 col4\" >11500</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow1_col5\" class=\"data row1 col5\" >54388</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow1_col6\" class=\"data row1 col6\" >348</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow1_col7\" class=\"data row1 col7\" >5.90%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow2_col0\" class=\"data row2 col0\" >7</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow2_col1\" class=\"data row2 col1\" >scala</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow2_col2\" class=\"data row2 col2\" >24259</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow2_col3\" class=\"data row2 col3\" >20833</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow2_col4\" class=\"data row2 col4\" >14036</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow2_col5\" class=\"data row2 col5\" >50972</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow2_col6\" class=\"data row2 col6\" >126</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow2_col7\" class=\"data row2 col7\" >2.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow3_col0\" class=\"data row3 col0\" >8</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow3_col1\" class=\"data row3 col1\" >cpp</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow3_col2\" class=\"data row3 col2\" >24225</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow3_col3\" class=\"data row3 col3\" >20833</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow3_col4\" class=\"data row3 col4\" >8962</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow3_col5\" class=\"data row3 col5\" >58333</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow3_col6\" class=\"data row3 col6\" >1691</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow3_col7\" class=\"data row3 col7\" >28.65%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow4_col0\" class=\"data row4 col0\" >9</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow4_col1\" class=\"data row4 col1\" >matlab</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow4_col2\" class=\"data row4 col2\" >24186</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow4_col3\" class=\"data row4 col3\" >22500</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow4_col4\" class=\"data row4 col4\" >8425</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow4_col5\" class=\"data row4 col5\" >60361</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow4_col6\" class=\"data row4 col6\" >254</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow4_col7\" class=\"data row4 col7\" >4.30%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow5_col0\" class=\"data row5 col0\" >11</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow5_col1\" class=\"data row5 col1\" >python</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow5_col2\" class=\"data row5 col2\" >23550</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow5_col3\" class=\"data row5 col3\" >20833</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow5_col4\" class=\"data row5 col4\" >8500</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow5_col5\" class=\"data row5 col5\" >50292</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow5_col6\" class=\"data row5 col6\" >2086</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow5_col7\" class=\"data row5 col7\" >35.34%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow6_col0\" class=\"data row6 col0\" >14</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow6_col1\" class=\"data row6 col1\" >java</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow6_col2\" class=\"data row6 col2\" >22158</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow6_col3\" class=\"data row6 col3\" >20000</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow6_col4\" class=\"data row6 col4\" >7764</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow6_col5\" class=\"data row6 col5\" >49417</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow6_col6\" class=\"data row6 col6\" >914</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow6_col7\" class=\"data row6 col7\" >15.49%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow7_col0\" class=\"data row7 col0\" >15</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow7_col1\" class=\"data row7 col1\" >javascript</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow7_col2\" class=\"data row7 col2\" >19369</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow7_col3\" class=\"data row7 col3\" >22500</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow7_col4\" class=\"data row7 col4\" >9000</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow7_col5\" class=\"data row7 col5\" >31816</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow7_col6\" class=\"data row7 col6\" >75</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow7_col7\" class=\"data row7 col7\" >1.27%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow8_col0\" class=\"data row8 col0\" >17</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow8_col1\" class=\"data row8 col1\" >c_sharp</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow8_col2\" class=\"data row8 col2\" >13683</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow8_col3\" class=\"data row8 col3\" >12500</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow8_col4\" class=\"data row8 col4\" >6487</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow8_col5\" class=\"data row8 col5\" >28833</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow8_col6\" class=\"data row8 col6\" >76</td>\n",
       "                        <td id=\"T_f470e51e_74f9_11ea_926f_701ce71031efrow8_col7\" class=\"data row8 col7\" >1.29%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15717dc1c88>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')\n",
    "data_pl=data_pl[data_pl['head_count']>50]\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head_counts=list(data_pl['percentage'].values)\n",
    "head_counts.append(other)\n",
    "head_counts\n",
    "\n",
    "labels=list(data_pl['pl_'].values)\n",
    "labels.append('其他')\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(head_counts, labels=labels, autopct='%1.0f%%')\n",
    "plt.title(f'算法工程师的编程语言')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 大数据工具 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >bd_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow0_col0\" class=\"data row0 col0\" >3</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow0_col1\" class=\"data row0 col1\" >flink</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow0_col2\" class=\"data row0 col2\" >27455</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow0_col3\" class=\"data row0 col3\" >27500</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow0_col4\" class=\"data row0 col4\" >11000</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow0_col5\" class=\"data row0 col5\" >37500</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow0_col6\" class=\"data row0 col6\" >22</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow0_col7\" class=\"data row0 col7\" >2.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow1_col0\" class=\"data row1 col0\" >4</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow1_col1\" class=\"data row1 col1\" >hive</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow1_col2\" class=\"data row1 col2\" >27310</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow1_col3\" class=\"data row1 col3\" >27500</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow1_col4\" class=\"data row1 col4\" >12000</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow1_col5\" class=\"data row1 col5\" >48194</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow1_col6\" class=\"data row1 col6\" >120</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow1_col7\" class=\"data row1 col7\" >12.27%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow2_col0\" class=\"data row2 col0\" >5</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow2_col1\" class=\"data row2 col1\" >spark</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow2_col2\" class=\"data row2 col2\" >26305</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow2_col3\" class=\"data row2 col3\" >25000</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow2_col4\" class=\"data row2 col4\" >8978</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow2_col5\" class=\"data row2 col5\" >49828</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow2_col6\" class=\"data row2 col6\" >351</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow2_col7\" class=\"data row2 col7\" >35.89%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow3_col0\" class=\"data row3 col0\" >6</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow3_col1\" class=\"data row3 col1\" >hadoop</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow3_col2\" class=\"data row3 col2\" >25618</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow3_col3\" class=\"data row3 col3\" >25000</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow3_col4\" class=\"data row3 col4\" >8950</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow3_col5\" class=\"data row3 col5\" >55000</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow3_col6\" class=\"data row3 col6\" >318</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow3_col7\" class=\"data row3 col7\" >32.52%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow4_col0\" class=\"data row4 col0\" >7</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow4_col1\" class=\"data row4 col1\" >storm</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow4_col2\" class=\"data row4 col2\" >24922</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow4_col3\" class=\"data row4 col3\" >17500</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow4_col4\" class=\"data row4 col4\" >4500</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow4_col5\" class=\"data row4 col5\" >53243</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow4_col6\" class=\"data row4 col6\" >43</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow4_col7\" class=\"data row4 col7\" >4.40%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow5_col0\" class=\"data row5 col0\" >8</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow5_col1\" class=\"data row5 col1\" >kafka</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow5_col2\" class=\"data row5 col2\" >24006</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow5_col3\" class=\"data row5 col3\" >23250</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow5_col4\" class=\"data row5 col4\" >3716</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow5_col5\" class=\"data row5 col5\" >41666</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow5_col6\" class=\"data row5 col6\" >57</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow5_col7\" class=\"data row5 col7\" >5.83%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow6_col0\" class=\"data row6 col0\" >9</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow6_col1\" class=\"data row6 col1\" >hbase</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow6_col2\" class=\"data row6 col2\" >21423</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow6_col3\" class=\"data row6 col3\" >17500</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow6_col4\" class=\"data row6 col4\" >12000</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow6_col5\" class=\"data row6 col5\" >54805</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow6_col6\" class=\"data row6 col6\" >54</td>\n",
       "                        <td id=\"T_f533f7ec_74f9_11ea_9deb_701ce71031efrow6_col7\" class=\"data row6 col7\" >5.52%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15717dc17c8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'bd_')\n",
    "data_pl=data_pl[data_pl['head_count']>10]\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "other=1-data_pl['percentage'].values.sum()\n",
    "head_counts=list(data_pl['percentage'].values)\n",
    "head_counts.append(other)\n",
    "head_counts\n",
    "\n",
    "labels=list(data_pl['bd_'].values)\n",
    "labels.append('其他')\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(head_counts, labels=labels, autopct='%1.0f%%')\n",
    "plt.title(f'算法工程师的大数据工具')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 机器学习工具 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >ml_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow0_col1\" class=\"data row0 col1\" >mxnet</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow0_col2\" class=\"data row0 col2\" >26561</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow0_col3\" class=\"data row0 col3\" >22500</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow0_col4\" class=\"data row0 col4\" >10533</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow0_col5\" class=\"data row0 col5\" >59970</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow0_col6\" class=\"data row0 col6\" >384</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow0_col7\" class=\"data row0 col7\" >9.90%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow1_col1\" class=\"data row1 col1\" >pytorch</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow1_col2\" class=\"data row1 col2\" >25094</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow1_col3\" class=\"data row1 col3\" >22500</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow1_col4\" class=\"data row1 col4\" >8992</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow1_col5\" class=\"data row1 col5\" >55062</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow1_col6\" class=\"data row1 col6\" >579</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow1_col7\" class=\"data row1 col7\" >14.93%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow2_col1\" class=\"data row2 col1\" >sklearn</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow2_col2\" class=\"data row2 col2\" >24416</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow2_col3\" class=\"data row2 col3\" >22500</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow2_col4\" class=\"data row2 col4\" >9070</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow2_col5\" class=\"data row2 col5\" >58333</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow2_col6\" class=\"data row2 col6\" >194</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow2_col7\" class=\"data row2 col7\" >5.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow3_col1\" class=\"data row3 col1\" >caffe</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow3_col2\" class=\"data row3 col2\" >24176</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow3_col3\" class=\"data row3 col3\" >20833</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow3_col4\" class=\"data row3 col4\" >9000</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow3_col5\" class=\"data row3 col5\" >50000</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow3_col6\" class=\"data row3 col6\" >861</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow3_col7\" class=\"data row3 col7\" >22.21%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow4_col1\" class=\"data row4 col1\" >keras</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow4_col2\" class=\"data row4 col2\" >24164</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow4_col3\" class=\"data row4 col3\" >20000</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow4_col4\" class=\"data row4 col4\" >8575</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow4_col5\" class=\"data row4 col5\" >55167</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow4_col6\" class=\"data row4 col6\" >278</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow4_col7\" class=\"data row4 col7\" >7.17%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow5_col1\" class=\"data row5 col1\" >tensorflow</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow5_col2\" class=\"data row5 col2\" >23793</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow5_col3\" class=\"data row5 col3\" >20944</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow5_col4\" class=\"data row5 col4\" >9000</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow5_col5\" class=\"data row5 col5\" >50000</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow5_col6\" class=\"data row5 col6\" >1361</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow5_col7\" class=\"data row5 col7\" >35.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow6_col0\" class=\"data row6 col0\" >8</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow6_col1\" class=\"data row6 col1\" >theano</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow6_col2\" class=\"data row6 col2\" >22044</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow6_col3\" class=\"data row6 col3\" >22500</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow6_col4\" class=\"data row6 col4\" >12500</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow6_col5\" class=\"data row6 col5\" >36357</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow6_col6\" class=\"data row6 col6\" >144</td>\n",
       "                        <td id=\"T_f602b0ba_74f9_11ea_a2b8_701ce71031efrow6_col7\" class=\"data row6 col7\" >3.71%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15717d64048>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'ml_')\n",
    "data_pl=data_pl[data_pl['head_count']>100]\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9803972143409851"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl['percentage'].values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "other=1-data_pl['percentage'].values.sum()\n",
    "head_counts=list(data_pl['percentage'].values)\n",
    "head_counts.append(other)\n",
    "head_counts\n",
    "\n",
    "labels=list(data_pl['ml_'].values)\n",
    "labels.append('其他')\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(head_counts, labels=labels, autopct='%1.0f%%')\n",
    "plt.title(f'算法工程师的机器学习库')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 英语"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_=data.shape[0]\n",
    "en=data[data['english']==1].shape[0]\n",
    "other=all_-en"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie([en,other], labels=['要求英语','没说'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_counts=data[~(data.edu=='')].groupby(by=['edu']).headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edu\n",
       "初中及以下       1\n",
       "博士        119\n",
       "大专        172\n",
       "本科       1805\n",
       "硕士        949\n",
       "高中          1\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(edu_counts.values, labels=edu_counts.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >edu</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow0_col0\" class=\"data row0 col0\" >博士</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow0_col1\" class=\"data row0 col1\" >30214</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow0_col2\" class=\"data row0 col2\" >16234</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow0_col3\" class=\"data row0 col3\" >30000</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow0_col4\" class=\"data row0 col4\" >51458</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow0_col5\" class=\"data row0 col5\" >119</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow0_col6\" class=\"data row0 col6\" >3.91%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow1_col0\" class=\"data row1 col0\" >硕士</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow1_col1\" class=\"data row1 col1\" >23211</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow1_col2\" class=\"data row1 col2\" >8654</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow1_col3\" class=\"data row1 col3\" >22500</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow1_col4\" class=\"data row1 col4\" >58333</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow1_col5\" class=\"data row1 col5\" >949</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow1_col6\" class=\"data row1 col6\" >31.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow2_col0\" class=\"data row2 col0\" >本科</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow2_col1\" class=\"data row2 col1\" >23209</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow2_col2\" class=\"data row2 col2\" >7500</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow2_col3\" class=\"data row2 col3\" >20833</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow2_col4\" class=\"data row2 col4\" >47688</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow2_col5\" class=\"data row2 col5\" >1805</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow2_col6\" class=\"data row2 col6\" >59.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow3_col0\" class=\"data row3 col0\" >大专</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow3_col1\" class=\"data row3 col1\" >12306</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow3_col2\" class=\"data row3 col2\" >3700</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow3_col3\" class=\"data row3 col3\" >9000</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow3_col4\" class=\"data row3 col4\" >40500</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow3_col5\" class=\"data row3 col5\" >172</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow3_col6\" class=\"data row3 col6\" >5.64%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow4_col0\" class=\"data row4 col0\" >初中及以下</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow4_col1\" class=\"data row4 col1\" >5250</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow4_col2\" class=\"data row4 col2\" >5250</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow4_col3\" class=\"data row4 col3\" >5250</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow4_col4\" class=\"data row4 col4\" >5250</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow4_col5\" class=\"data row4 col5\" >1</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.03%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow5_col0\" class=\"data row5 col0\" >高中</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow5_col1\" class=\"data row5 col1\" >4750</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow5_col2\" class=\"data row5 col2\" >4750</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow5_col3\" class=\"data row5 col3\" >4750</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow5_col4\" class=\"data row5 col4\" >4750</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow5_col5\" class=\"data row5 col5\" >1</td>\n",
       "                        <td id=\"T_fc76ef46_74f9_11ea_b1cd_701ce71031efrow5_col6\" class=\"data row5 col6\" >0.03%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15711c831c8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats_by_col(data[data.edu.isin(['初中及以下','高中','大专','本科','硕士','博士'])], 'edu')\n",
    "apply_style(data_edu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_associate=data[data.edu=='大专'].monthly_salary\n",
    "salary_bachelor=data[data.edu=='本科'].monthly_salary\n",
    "salary_master=data[data.edu=='硕士'].monthly_salary\n",
    "salary_phd=data[data.edu=='博士'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大专，本科，硕士，博士的平均工资分别是12972，22552，23411，31686。中位数分别是10708， 20000，20916， 30000。\n"
     ]
    }
   ],
   "source": [
    "print('大专，本科，硕士，博士的平均工资分别是{:.0f}，{:.0f}，{:.0f}，{:.0f}。中位数分别是{:.0f}， {:.0f}，{:.0f}， {:.0f}。'.format(\n",
    "    salary_associate.mean(),salary_bachelor.mean(),salary_master.mean(),salary_phd.mean(),\n",
    "    salary_associate.median(),salary_bachelor.median(),salary_master.median(),salary_phd.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=20.249757477133127, pvalue=9.534846696218622e-06)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=1.2120891408112546, pvalue=0.2712194733243708)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=40.000183419534785, pvalue=4.711352280836977e-10)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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beOedd7j33ntjxn/dddcxf/58pkyZQllZGccffzwAGzduZOXKlcycOZPly5fHXf7s7GzWrFlDQUEBixYtilln69atvPDCC/z5z39m/vz5vPzyy7z22muUlZWxfv16Zs+ezY033gjA1KlT6d69O/fccw/vv/9+3OVNxCmnnMLKlSs577zzWLVqFYMGDYpb98MPP2TSpEksWbKEb3zjG3HrTJs2jWeffZbKyko++ugjoPFn4LnnnuPNN98kOzubF154gYqKCm6++WYAPvvsM77++mv69u0bN44777yTiRMnsm7dOj777DOeeeYZAC699FLWr1/Ptm3b4o42t23bRkZGBmvXruWqq65i7969cfvZsGEDQ4cOZeXKlQBMmDCB0tJSANauXcsll1zCwoULGTBgAGvXruXDDz9k69atTJ48mcceewyAlStX8t3vfjduHyLQ8gihblQwB7iF6AnlmcAd4YYVjsmTo1+WOumkk/j668RPg5xzzjkADBw4kMrKSvr06cOUKVO4+OKLefjhh+NulAB+97vfNdp7jlXWs2dPvvrqKwYPHnxI3UR8//vfJzMzM+Zy1dTU1D+vO4QzaNAg3n777Zhtvfnmm+Tl5dXXnzx5Mm+88QbnnnsuZsaQIUN44403AMjJyaFbt27k5OSQkZERd3mbUxffoEGDWLp0KZdeeilPPPFE/TqP5d/+7d/Izs7m3XffjdteZmYmDz30EJMmTWL37t315U0/AyeccAKbN2+moKCA6667rr6dBx98kGuuuabZ2Ldt21a/TgcPHly/XhJZz4MGDWLAgAGMGTOGlStX0rNnz5jLATBgwIBGI8/+/ftTVVXF559/Tu/evenVqxdvvfUWy5Yto7CwkL/+9a988MEHnHrqqezZs4eysjIGDBhAjx49ml0ekWYTgrv/0t1/GX3qF7n7ze5+CVDbPuElV69evRpN9+jRgy+++AKIHraJV/byyy8D8Morr5Cbm8v7779Pnz59WLlyJSeccMIho4I6X331Fe+//z6nnXZas2Uvv/wyY8eO5aWXXuKGG25odhlixdd0ubp3715/OKluTxJg8+bNQHQUkJOTE7P9008/vb7e+vXr+eEPf8gZZ5zBxo0bcXc2bNjAmWfGPoUUa9liaRhf3V712WefzXPPPceoUaNYuXJlsyOE2bNnc9999zF79uy47S1atIjx48ezZMmSRuun6bp65plnmD17Ni+++GJ9Itu5cyeZmZkce+yxzS7HmWeeyYYNGwAarZdE1vOrr75Kfn4+q1at4pNPPmHdunUxlwPg6KMPvd7jvPPOY/78+VxxxRUAfOc73+H666+nrKyMefPm1Z/3mDhxIldffXV9IhRpTqLnEA6a2XQzu9DMOuT5g1hGjx7NH/7wB/Lz81m3bl3cso0bN1JYWMinn37KZZddRr9+/VixYgX5+fn1G7E6DffkHn744UNOQMYqO/3007n33nsZPnw448aNq+83lrPPPpu33nqLYcOG8fvf/z5mnREjRrBixQqmTp3KgQMH6svLy8spLCxk9erV/OhHPzokXoiOgoYOHUp+fj633HILt912GwMGDKCwsJD8/Hxuv/127rrrrpj9xlq2OjU1NRx55JEAXHHFFSxYsICf/vSn9OkTPfo4aNAgTj75ZE455RS++c1vcvLJJ8ddB0cddRQnnngip59+OsuXL4/Z3ujRo7nrrrsYMWIEAB988EHMts4++2ymTZvGiBEjmDhxIq+//joPPfRQ3NFBw+W46aabePzxx7ngggvo3bs3Y8aMAeA///M/yc/P59vf/nbckU5OTg4lJSWcf/757Nixg7y8vJjLEc+ECROYP38+l112GQA//vGPKS0tpaCggPvvv58TTzwRgPHjx2NmXHDBBc22JwJE9zJbegC9gVuBN4CbgOxEXhfm45xzzvH2cNttt/maNWsO67UfffRRQmVPPfWUFxQU+KhRo/zyyy/3JUuWHFZ/qRZr2b766isfPHiwDx061J9//vkURNW8hQsX+vDhw33MmDF+6aWX+po1a2IuxzvvvONDhgzx/Px837ZtWwoibb3XX3/dzz33XH/ooYdSHYqkEWCTx9mumgeHHZpjZguAbwHfBmYD17r7FWElqUTk5eX5pk2bUhlCh1RSUkJFRUVS26wKrlzObscrl3Nzc9Pi4rtENP2W1DHHHMOTTz6ZmmCkyzOzze6eF2teolcq/527F5rZc+7+RzP7RRLjk3ZUUVHBK395JTrmS5bgS1Y7bWcSG23Gp+3TTbKk+3UjInUSTQg7zexW4FgzKwZ2hBiThK03HCw8mLTmMsqip6KS2WYi/YlIciX6nzWZ6H7gi8AxwFWhRSQiIimR0AjB3WuA34Qci4iIpJDG3iIiAighiIhIQAlBREQAJQQREQkoIYiICKCEICIigS6fEEpKSigpKUl1GCLN0udU2kOiVyp3Wsm+r49IGPQ5lfbQ5UcIIiISpYQgIiKAEoKIiASUEEREBFBCEBGRgBKCSCf2wAMPUFBQwKJFi5LW5vbt2ykqKkqLbz61FEuisVZXVzNt2jR27drVYp+tqRuGMPsPJSGYWbfgr5nZEWaW0WCeNZwWkfA8+uijAEQikaS1OW/ePPbt28fcuXOT1mZYsSQaayQSYevWrQmtp9bUDUOY/Sd9w2xmfYFSM9sFPAP8Evijmb1uZluAVcDoJq+ZZWb/kuxYRLqyBx54oNF0MkYJ27dvp7KyEoDKysqUjhJaiiXRWKurqyktLcXdKS0tbXbPuzV1wxB2/0m/MM3dPzKzK4Cn3P0iADPrDcwCngP+7O5NfxX3S2B/smNJRFVVFTU1NR3mB9vbqry8HNrnly7Dsze6HF3lPYPo8vbo0aNVr6kbHdSJRCJcc801bYpj3rx5jabnzp3LI4880qY2w4ol0VgjkQjuDsDBgweJRCLMmDEjZp+tqRuGsPsP69DNJcBqADM7AngV6Af8AHjVzOxwGjWzn5jZJjPbtHNnO/2gu4jUq9vjjjfdnlqKJdFYV69eTW1tLQC1tbWsWrUqbp+tqRuGsPtP+gghSACzgW5m9jMgH9hI9Cc4DwR9TjCzmcDXwcv+D3DAzKYE00cBt7v7fzVs290XAgsB8vLyPBnxZmdnA3SZ+8RMnz6dVz54JdVhtM3RcNoJp3WZ9wxIm9FQTk5Oow1rTk5O2saSaKyjR4/m6aefpra2lszMTMaMGRO3z9bUDUPY/YcxQria6Ib+F8BT/O9GfxDw9wDuvtTdB7n7EHcfAiwAflU37e5nNU0GItI6kyZNajRdXFzc5jZnzZrVaPrWW29tc5uHq6VYEo21uLiYuoMWGRkZza6n1tQNQ9j9h5EQIsB9DabrDg+dBLwZUp8i0sS1117baLqt5w8A+vfvX7+nnZOTQ25ubpvbDCuWRGPNysqiqKgIM6OoqIg+ffrE7bM1dcMQdv9J3zi7+1fB018BY4FMoongQqJJoabua6kiEq66UUIy9yRnzZpFr169Ujo6SDSWRGMtLi5m4MCBCa2n1tQNQ5j9h3X7633Az4AzgKOBXOAxot8mOh841cwqgQPufqDhC4NkkeHutSHFJtJlXHvttYeMFNqqf//+lJaWJrXNw9VSLInGmpWVxYIFCxLqszV1wxBm/6EkBHd/Ini63sx+BKxw90cBzGw30fMKvwRGmln9lyCDk8oZwFLg12HEJiIisYX+Aznu/lCT6XXB05vC7ltERBKnE7wiIgIoIYiISEAJQUREgHY4h5DuUvk9apFE6XMq7aHLJ4R0uSWASHP0OZX2oENGIiICKCGIiEhACUFERAAlBBERCSghiIgIoIQgIiIBJQQREQGUEEREJNDlL0zrkj6FjLIk7gt8Gv2T1DZb6u+E9ulKpCtRQuhiwrgFQpVXAZB9QnbS247pBN3KQSQMSghdjG6BICLx6ByCiIgASggiIhJQQhAREUAJQUREAkoIIiICKCGIiEhACUFERAAlBBERCejCNEmZkpISKioqQu2jqiq4ijq7na6ijiE3N1cXBEqHoIQgKVNRUcGbW7bQL8Q+9gR/P62uDrGX+HakpFeRw6OEICnVD7gGC639RTiE3Eci/Yt0BDqHICIigBKCiIgElBBERARQQhARkYASgoiIAEoIIiISUEIQERFACUFERAJKCJ1cSUkJJSUlqQ5D5BD6bKYfXancyYV9ryCRw6XPZvrRCEFERAAlBBERCSghiIgIoIQgIiIBJQQREQGUEEREJNCuCcHMlIBEpNOqrq5m2rRp7Nq1K9WhHJZQNtBmNsfMhppZXzM70cx+bma/AG4yswvNrEeT+rPM7F/CiEVEpL1EIhG2bt1KJBJJdSiHJcw99lpgOPB94FxgEPAVMBjo26Tul8D+EGMREQlVdXU1paWluDulpaUdcpSQ9CuVzewU4FvAmcCxwBTAgsdJwDZ3r0x2vxJbVVUVNTU1TJ8+PdWhHKK8vLzTn8TaBewsL0/L9Z9q5eXl9OjRo+WKHUQkEsE9+hvaBw8eJBKJMGPGjBRH1Tph/D+eDJwAnAEcBbwDVABvA68A+83se2b2P2a2wcw2ANOAX9RNm9kWMxvXtGEz+4mZbTKzTTt37gwhdBGRw7N69Wpqa2sBqK2tZdWqVSmOqPWSPkJw9zVmdiHwFPB3wKmAEx0hHAdsdfelwNK615jZ/wX2uvv9LbS9EFgIkJeX58mOvTPKzs4GSMubiE2fPp1Pt2xJdRih6gP0Pu20tFz/qdbZRk2jR4/m6aefpra2lszMTMaMGZPqkFot7JvbfQD8FMgkOhrpDXQPuU8RkXZXXFxMaWkpABkZGRQXF6c4otYLMyH0BOYBX/O/J4xzgE/M7Cx3vyHEvkVE2lVWVhZFRUUsX76coqIi+vTpk+qQWi3MhPCFu59nZvcCq9291MzmAE+5+yYz6w4ccPcDDV9kZt2ADHevDTE2EZGkKy4uprKyskOODiC8hNCtQdt3A8eY2UXA94CHg/JfAiPN7GDdi8xsCtFDS0uBX4cUm4hIKLKysliwYEGqwzhsoSQEd5/d4PnHwMdm9jFwvrt/GpTfFEbfIiJyeNrtF9Pc/bP26ktERFqvs18XJCIiCVJCEBERQAlBREQC7XYOQVIjNzc31SGIxKTPZvpRQujkOtvtAaTz0Gcz/eiQkYiIAEoIIiISUEIQERFACUFERAJKCCIiAighiIhIQAlBREQAXYcgKbYDWER4v4b6YfA3zD6as4PozwSKdARKCJIy7XGl6t6qKgB6B78t3d56oytypeNQQpCU0ZWqIulF5xBERARQQhARkYASgoiIAEoIIiISMPfUfB2vrcxsJ/BugtWzgOoQw0mmjhJrR4kTOk6sHSVOUKxhaK84T3b342PN6LAJoTXMbJO756U6jkR0lFg7SpzQcWLtKHGCYg1DOsSpQ0YiIgIoIYiISKCrJISFqQ6gFTpKrB0lTug4sXaUOEGxhiHlcXaJcwgiItKyrjJCEBGRFighSIdkZseZ2Wgzy0p1LCKdRadPCGa2yMxeNLNZKYyhr5mtC55nmtkKM1tvZle3tSyJMR5jZqVmtsrMlplZ91jrri1lSYz1WOAp4DxgjZkdn66xBu33NbNX2hpTyOv0CDN7z8zKgsffmdkvzWyjmf17g3qHXZbkeH9rZpcHz9N1nf6swfrcYmYPpGusdTp1QjCzK4Fu7j4UOMXMTktBDMcCEaBXUDQN2Ozu+cB4M/tGG8uSZRJwr7uPIXob/4k0WXex1meiZUmME2AgMMPd7wBWAiPSOFaAXwM92hJTO63TJe5e6O6FQHfgAqJJ92MzG2Vm5xxuWTIDNbNhQD93X5HO69Td72uwPtcBb6drrHU6dUIACoGlwfNVRD+k7e0A8I/A5zFiWgvktbEsKdz9t+6+Opg8HvgnDl13hW0oSxp3f97dN5hZAdGNzkXpGquZjQD2EU2ybYkp1DiBIcBlZvaymS0CRgL/5dFvnawEhgEXtqEsKcwsE3gQqDSz75Le67Qu5hOAvkB2usfa2RNCL+CD4Pluom9Ku3L3z939sxZiaktZUpnZUOBY4P00j9OIJtpPAE/HWM2sOzAbmBkUpfN7vxEY5e7nAZlAjzSNdTKwDfgV0Z2BqWkaZ0NTgfvaGGs9r5wAAAOJSURBVFe7xNrZE8Jeoh9sgKNJj+WNFVNbypLGzI4DFgBXp3OcAB41FdgKnJ+msc4EfuvunwbT6bxOt7p73S+ObkrjWM8GFrr7DuB3REfK6RgnAGaWAQwHytoYV7tsy9JhAxmmzfzv0OrvgcrUhVIvVkxtKUuKYG/2CeAmd383XeMMYr3RzCYHk72Bu9M01lHAVDMrA84CLk/TOAH+w8z+3sy6Af9AdI80HWOtAE4JnucBOWkaZ51hwEvB4bO0/Z+q5+6d9gH8DfAqcC/wBnBMCmMpC/6eDPwF+A3RYXq3tpQlMb6fET38UhY8ipuuu1jrM9GyJK/LY4HVRPcOfxv0mZaxNnz/2xJTO6zTAURHW68BdxDdWVwffNbeAr7dlrIkxvkNojsua4EXg/+JtFynQbx3AlcGz9P2/a+PN4xG0+kRbDy+R/RbCSmPJ4jpW0FMxySjrD3XXVvKFGvHiTPorwcwHjglGWVap+kfq25dISIiQOc/hyAiIglSQhAREUAJQUREAkoIIiICKCGIAGBmhWaW02B6ccPpJnXL2iGe0PsQaUoJQSSqkOhFTiJdlhKCdBpmttmit/B+0sxeMrObLXq78HVmNj+os9jMbg3K/mxmPczsYWAKMN/MHm3Q5OSG9eL0GTGzIQ3aHhKn3jfNbI2ZvWBmDwRl3wqm15nZHc0s1yH1zCzHzB41s4eD+BOORSQeJQTpTHoCE4jeyvkHwGnA4+4+DDjGzC4O6h0dlL0JnO3uVwGLgevdfVKD9hrVi9PnI8Ck4NYff+vuG+LUGwa85u4XAGuDe9ycQPR+R0VEb2sRT7x6lwMPBPG3JhaRmJQQpDP5yN33Au8Sve14X+ClYN5LwN8GzyPB3/eI3vc/nkTqrQGGApcCy5tpqxToZmargdPd/SCwn+iG/iGit2SIJ169VU02+onGIhKTEoJ0ZvuI3uef4O9fGpQ3VUN0hFF3a+149RoJNuyrif4Izu+aqToU+A93Hw2MMLNTgRnAXcCPiN7CO5549fYeZiwiMSkhSGd2JDDRzF4APnX3Vc3U/S9gppltAE5tZT9PAO959C6x8fwV+JWZvQh8THQU8xRwP9G9+S+CH1KJJdF6icYiEpPuZSTSBmY2kuiPtdzi7s8oFunIlBBEksjM+gGPNyl+y92vTUU8Iq2hhCAiIoDOIYiISEAJQUREACUEEREJKCGIiAgA/x+ZK7CYRpCzPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['大专','本科','硕士','博士']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >experience</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow0_col0\" class=\"data row0 col0\" >5_10</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow0_col1\" class=\"data row0 col1\" >31399</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow0_col2\" class=\"data row0 col2\" >8840</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow0_col3\" class=\"data row0 col3\" >33333</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow0_col4\" class=\"data row0 col4\" >60000</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow0_col5\" class=\"data row0 col5\" >297</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow0_col6\" class=\"data row0 col6\" >8.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow1_col0\" class=\"data row1 col0\" >10+</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow1_col1\" class=\"data row1 col1\" >28325</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow1_col2\" class=\"data row1 col2\" >1000</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow1_col3\" class=\"data row1 col3\" >35000</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow1_col4\" class=\"data row1 col4\" >41666</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow1_col5\" class=\"data row1 col5\" >21</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow1_col6\" class=\"data row1 col6\" >0.62%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow2_col1\" class=\"data row2 col1\" >24646</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow2_col2\" class=\"data row2 col2\" >11000</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow2_col3\" class=\"data row2 col3\" >22500</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow2_col4\" class=\"data row2 col4\" >50333</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow2_col5\" class=\"data row2 col5\" >884</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow2_col6\" class=\"data row2 col6\" >26.08%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow3_col0\" class=\"data row3 col0\" >1_3</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow3_col1\" class=\"data row3 col1\" >21999</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow3_col2\" class=\"data row3 col2\" >7000</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow3_col3\" class=\"data row3 col3\" >20000</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow3_col4\" class=\"data row3 col4\" >44125</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow3_col5\" class=\"data row3 col5\" >881</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow3_col6\" class=\"data row3 col6\" >26.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow4_col0\" class=\"data row4 col0\" ></td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow4_col1\" class=\"data row4 col1\" >19951</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow4_col2\" class=\"data row4 col2\" >5250</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow4_col3\" class=\"data row4 col3\" >17500</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow4_col4\" class=\"data row4 col4\" >45833</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow4_col5\" class=\"data row4 col5\" >1306</td>\n",
       "                        <td id=\"T_008e3e2e_74fa_11ea_b04c_701ce71031efrow4_col6\" class=\"data row4 col6\" >38.54%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15717b28608>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats_by_col(data, 'experience')\n",
    "apply_style(data_experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience=='10+'].monthly_salary\n",
    "salary_we_5_10=data[data.experience=='5_10'].monthly_salary\n",
    "salary_we_3_5=data[data.experience=='3_5'].monthly_salary\n",
    "salary_we_1_3=data[data.experience=='1_3'].monthly_salary\n",
    "salary_we_no=data[data.experience=='no'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uBeDVV18lJyeH999/n969e7NkyRL69+9/xF5Bnc8++4z333+fAQMGNNu2du1arrjiCl555RV+/OMfN7sO4fI1Xa+uXbvWv51U90oSYMOGDUBoLyA7Ozvs8k8//fT6fqtXr+bqq6/mjDPOYN26dbg7a9as4cwzz4x6fcNpmK/uVfU555zDiy++yOjRo1myZEmzewizZ8/mgQceYPbs2RGXN3/+fCZMmMDChQsbbZ+m2+qFF15g9uzZvPzyy/WFbMeOHWRmZtKrV69m1+PMM89kzZo1AI22SzTb+bXXXiMvL4+lS5fy8ccfs2rVqrDrAdCjx5HnUJx//vnMmTOHyy67DICvfvWr3HDDDZSXl3PnnXfWH/eYNGkS11xzTX0hFGlOpyoITY0ZM4ann36avLw8Vq1aFbFt3bp1FBQUsGfPHsaPH0+/fv1YvHgxeXl59U9idRq+knv44YePOAAZru3000/nvvvuY+TIkVx11VX144Zzzjnn8PbbbzNixAieeOKJsH1GjRrF4sWLmT59Op9//sV8hBUVFRQUFLBs2TK+//3vH5EXQntBw4cPJy8vj5tvvpnbbruNQYMGUVBQQF5eHj//+c+55557wo4bbt3q1NTU8OUvfxmAyy67jLlz5/LDH/6Q3r17A6FXzCeffDKnnnoqJ5xwAieffHLEbXDUUUdx4okncvrpp/Pss8+GXd6YMWO45557GDUqtDP7wQcfhF3WOeecw4wZMxg1ahSTJk1i8+bNPPTQQxH3Dhqux0033cSiRYu48MIL6dmzJ2PHjgXgD3/4A3l5eZxyyikR93Sys7MpKSnhggsuYPv27eTm5oZdj0gmTpzInDlzGD9+PADXXnstZWVl5Ofn89vf/pYTTzwRgAkTJmBmXHjhhc0uTwQIvcps6QvYGE2/RH6de+65ngi33XabL1++vE2/++GHH0bV9txzz3l+fr6PHj3aL730Ul+4cGGbxku2cOv22Wef+dChQ3348OG+YsWKJKRq3rx583zkyJE+duxYHzdunC9fvjzserz77rs+bNgwz8vL8y1btiQhaett3rzZzzvvPH/ooYeSHUVSCLDeIzyvmkfxCVIzuwE47O4lca5PUcvNzfX169cnO0aHUlJSQmVlZUyXWR2cKZyVwDOFc3JyUupauk0/JXXsscfyzDPPJCeMdHpmtsHdc8P9LNozlS8ndJbyd4EawD1NDyxLZJWVlbz6xqvQM4YLDT6AtcN2xHChzdiTmGFaI9XPGxGpE+01lUe23Es6hJ5wuOBwzBaXUR46TBXLZUYznoi0XlQFwcwMGAf0BbYAW939n/EMJiIiiRXty6kngJGE5jDKIDT9tYiIdCDRFoTj3f3HwH53X92K3xMRkTQR7RN73fTXX9H01yIiHVO0B5WvM7PLgbeCrzvimkpERBIuqj2E4AppGUAt0CGmvhYRkcaifctoEaFrKH8KXAL8Pm6JREQkKaI9Me0Ed/9W3R0zWx6nPCIikiTRFoQDZjYL2ACcD+w1s3x3Xxm/aIlRUhKajSOVpjoQiRU9vqU1oi0IrwBfBi4I7r8KFABpXxBiPXePSCrR41taI9qC8JC7188fbGZnu3vsr0QvIiJJE+1B5afM7H+b2b+Z2S+Bu1v8DRERSSvRFoQ84BRgB6GzlS9pob+IiKSZ1sxldBShYwhnmdmv4xdJRESSIdpjCPcTmtm+P3AzEPmCtyIikpai3UOYBPwMuIfQW0ffar67iKSTnTt3MmPGDHbt2tVi33feeYfCwsJ2f4Kp4ZhtXWZLuWOVtbOItiB8zd2vAva4+5+BY1sziJkdZ2ZjzKxPqxOKSNyVlpayadMmSktLW+x755138umnn3LHHe2b0qzhmG1dZku5Y5W1s4i2IOwws1uBXmZWBGyPdgAz6wU8R+iEtuVmdnyYPl8ys/fMrDz4+lq0yxeR9tm5cydlZWW4O2VlZc3uJbzzzjtUVVUBUFVV1eZX3g3H/POf/9ymZbaUO1ZZO5NojyFMAa4DXia0dzCtFWMMBma6+5qgOAwBloTps9Ddb2zFcmOiurqampoanckJVFRUQGKudBk/+0Prob9nSEVFBd26dWu2T2lpKe6hOSsPHz5MaWkpM2fODNv3zjvvbHT/jjvu4JFHHml1roZj1tbWtmmZLeWOVdbOJKo9BHevcff73f1/unuJux+IdgB3XxEUg3xCewkvh+k2DBhvZmvNbL6ZhS1UZnadma03s/U7diToou0iHdyyZcvqn5Rra2tZunRpxL51r7gj3W/LmC2NEc0ywuWOVdbOJNo9hHYJrsn8beBjQlNoN7UOGO3u28zsEUIzqj7btJO7zwPmAeTm5sZkGu6srCzgizlfOrPi4mJe/eDVZMdonx4woP8A/T0D0ewpjRkzhueff57a2loyMzMZO3ZsxL7Z2dmNnlizs7PblKvhmOHGaO0ywuWOVdbOJCGXwvSQ6cAm4LIwXTa5+7bg9npgQCJyiQgUFRURes0GGRkZFBUVRex7yy23NLp/6623tnvMzMzMNi2zpdyxytqZxL0gmNmNZjYluNsT2BOm26NmdpaZdQG+CbwW71wiEtKnTx8KCwsxMwoLC+ndu3fEvgMHDqx/pZ2dnU1OTk67xxw3blybltlS7lhl7UwSsYcwD7jazFYCXYBwb1DeATwKbARedve/JCCXiASKiooYPHhws3sHdW655Ra6d+/e7lfcDcds6zJbyh2rrJ1F3I8huPvHwJgW+mwm9EkjEUmCPn36MHfu3Kj6Dhw4kLKyspiO2bt37zYts6XcscraWSTkoHJDZlbepGmvu1+e6BwiItJYwguCuxckekwREWlZQj5lJCIiqU8FQUREABUEEREJJPwYQqrRZ5OlI9PjW1qj0xcETYImHZke39IaestIREQAFQQREQmoIIiICKCCICIiARUEEREBVBBERCSggiAiIoAKgoiIBDr9iWnSxB7IKI/h64Tg+ngxXWZL4/VPzFAiHY0KgtSLxzQH1V4NQFb/rJgvO6z+mq5BpK1UEKSepjkQ6dx0DEFERAAVBBERCaggiIgIoIIgIiIBFQQREQFUEEREJKCCICIigAqCiIgEdGJamispKaGysjKmy6yuDs4uzkrQ2cVtkJOToxPpRGJMBSHNVVZW8tbGjfSL4TL3Bd/37NwZw6XGzvZkBxDpoFQQOoB+wPewmC1vPg4xXmYs1eUTkdjSMQQREQFUEEREJKCCICIigAqCiIgEVBBERARQQRARkYAKgoiIACoIIiIS6PQFoaSkhJKSkmTHEIk7PdalJZ3+TOVYzwMkkqr0WJeWdPo9BBERCVFBEBERQAVBREQCKggiIgKoIIiISEAFQUREgBQrCGb272Y22syOSXYWEQlv586dzJgxg127diU7Ssy0Zp2Svf7xHD9hBcHM+prZqmZ+PhB4AsgDVphZ10RlE5HolZaWsmnTJkpLS5MdJWZas07JXv94jp+QgmBmvYBSoHsz3QYD09z9Z8A/gFMSkU1Eordz507Kyspwd8rKyjrEXkJr1inZ6x/v8RN1pvLnwLeBZyJ1cPc/mNmXzGwc0AtIyGmV1dXV1NTUUFxcnIjhYq6ioiK13vdLgF3AjoqKtP2bJUtFRQXdunVr1zJKS0txD13T+vDhw5SWljJz5sxYxEua1qxTstc/3uMn5LnE3T9x971RdO0BfAvYCkdeSd3MrjOz9Wa2fseOHbGOKSItWLZsGbW1tQDU1taydOnSJCdqv9asU7LXP97jp9RcRu6+Bygys0eB84BXmvx8HjAPIDc394iC0RZZWVkAaTvpV3FxMXs2bkx2jITqDfQcMCBt/2bJEos9qjFjxvD8889TW1tLZmYmY8eOjUGy5GrNOiV7/eM9fsq822BmD5hZfnC3J7AnmXlE5EhFRUWYGQAZGRkUFRUlOVH7tWadkr3+8R4/ZQoC8Avg7uCTSGvd/e1kBxKRxvr06UNhYSFmRmFhIb179052pHZrzTole/3jPX5C3zJy94JmfvYucGHi0ohIWxQVFVFVVdUh9g7qtGadkr3+8Rw/4ccQzKwfsKhJ89vu/oNEZxGR1uvTpw9z585NdoyYas06JXv94zl+wguCu28HChI9roiINC+VjiGIiEgSqSCIiAiggiAiIgEVBBERAVLsTOVkyMnJSXYEkYTQY11a0ukLgiZIk85Cj3Vpid4yEhERQAVBREQCKggiIgKoIIiISEAFQUREABUEEREJqCCIiAig8xA6hO3A/CMvQd1m24LvsVxmLG0ndEk9EYktFYQ0F4+zT/dXVwPQM7jedKrpic66FYkHFYQ0p7NPRSRWdAxBREQAFQQREQmoIIiICKCCICIiAXNPzY8WtsTMdgBbo+zeB9gZxzixlC5Z0yUnpE/WdMkJyhoPicp5srsfH+4HaVsQWsPM1rt7brJzRCNdsqZLTkifrOmSE5Q1HlIhp94yEhERQAVBREQCnaUgzEt2gFZIl6zpkhPSJ2u65ARljYek5+wUxxBERKRlnWUPQUREWqCCIGnJzI4zszFm1ifZWUQ6ig5fEMxsvpm9bGa3JDFDXzNbFdzONLPFZrbazK5pb1sMMx5rZmVmttTM/mhmXcNtu/a0xTBrL+A54HxguZkdn6pZg+X3NbNX25spztv0S2b2npmVB19fM7Ofmdk6M/u/Dfq1uS3GeX9jZpcGt1N1m/5ng+250cx+l6pZ63TogmBmVwJd3H04cKqZDUhChl5AKdA9aJoBbHD3PGCCmR3TzrZYmQzc5+5jCV1yYBJNtl247RltWwxzAgwGZrr7XcASYFQKZwX4JdCtPZkStE0XunuBuxcAXYELCRXdj8xstJmd29a2WAY1sxFAP3dfnMrb1N0faLA9VwF/T9WsdTp0QQAKgCeD20sJPUgT7XPg28AnYTKtBHLb2RYT7v4bd18W3D0e+B8cue0K2tEWM+6+wt3XmFk+oSedb6RqVjMbBXxKqMi2J1NccwLDgPFmttbM5gMXAf/toU+dLAFGAF9vR1tMmFkm8CBQZWaXk9rbtC5zf6AvkJXqWTt6QegOfBDc3k3oj5JQ7v6Ju+9tIVN72mLKzIYDvYD3UzynESq0HwOeilnNrCswG5gVNKXy334dMNrdzwcygW4pmnUKsAX4BaEXA9NTNGdD04EH2pkrIVk7ekHYT+iBDdCD1FjfcJna0xYzZnYcMBe4JpVzAnjIdGATcEGKZp0F/Mbd9wT3U3mbbnL3uqunrk/hrOcA89x9O/AYoT3lVMwJgJllACOB8nbmSshzWSo8QcbTBr7YtToLqEpelHrhMrWnLSaCV7NPATe5+9ZUzRlkvdHMpgR3ewL3pmjW0cB0MysHzgYuTdGcAI+a2Vlm1gX4JqFXpKmYtRI4NbidC2SnaM46I4BXgrfPUvZ/qp67d9gv4N+A14D7gDeBY5OYpTz4fjLwBnA/od30Lu1pi2G+/yT09kt58FXUdNuF257RtsV4W/YClhF6dfibYMyUzNrw79+eTAnYpoMI7W29DtxF6MXi6uCx9jZwSnvaYpjzGEIvXFYCLwf/Eym5TYO8dwNXBrdT9u9fnzceC02lr+DJ41uEPpWQ9DxBpq8EmY6NRVsit1172pQ1fXIG43UDJgCnxqJN2zT1s2rqChERATr+MQQREYmSCoKIiAAqCCIiElBBEBERQAVBBAAzKzCz7Ab3FzS836RveQLyxH0MkaZUEERCCgid5CTSaakgSIdhZhssNIX3M2b2ipn91ELTha8yszlBnwVmdmvQ9jcz62ZmDwNTgTlm9vsGi5zSsF+EMUvNbFiDZQ+L0O8EM1tuZi+Z2e+Ctq8E91eZ2V3NrNcR/cws28x+b2YPB/mjziISiQqCdCRHAxMJTeX8XWAAsMjdRwDHmtnFQb8eQdtbwDnuPg1YANzg7pMbLK9RvwhjPgJMDqb++A93XxOh3wjgdXe/EFgZzHHTn9B8R4WEprWIJFK/S4HfBflbk0UkLBUE6Ug+dPf9wFZC0473BV4JfvYK8B/B7dLg+3uE5v2PJJp+y4HhwDjg2WaWVQZ0MbNlwOnufhg4ROiJ/iFCUzJEEqnf0iZP+tFmEQlLBUE6sk8JzfNP8P2NBu1N1RDaw6ibWjtSv0aCJ/ZlhC6C8x3o67QAAACrSURBVFgzXYcDj7r7GGCUmZ0GzATuAb5PaArvSCL129/GLCJhqSBIR/ZlYJKZvQTscfelzfT9b2CWma0BTmvlOE8B73lolthI/gH8wsxeBj4itBfzHPBbQq/mDwQXUgkn2n7RZhEJS3MZibSDmV1E6GItN7v7C8oi6UwFQSSGzKwfsKhJ89vu/oNk5BFpDRUEEREBdAxBREQCKggiIgKoIIiISEAFQUREAPj/0mTnqUoOrhoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['10+','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >company_type</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow0_col0\" class=\"data row0 col0\" >外企代表处</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow0_col1\" class=\"data row0 col1\" >33333</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow0_col2\" class=\"data row0 col2\" >33333</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow0_col3\" class=\"data row0 col3\" >33333</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow0_col4\" class=\"data row0 col4\" >33333</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow0_col5\" class=\"data row0 col5\" >2</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.06%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow1_col0\" class=\"data row1 col0\" >外资（欧美）</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow1_col1\" class=\"data row1 col1\" >28554</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow1_col2\" class=\"data row1 col2\" >8000</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow1_col3\" class=\"data row1 col3\" >27500</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow1_col4\" class=\"data row1 col4\" >60000</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow1_col5\" class=\"data row1 col5\" >241</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow1_col6\" class=\"data row1 col6\" >7.11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow2_col0\" class=\"data row2 col0\" >外资（非欧美）</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow2_col1\" class=\"data row2 col1\" >24032</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow2_col2\" class=\"data row2 col2\" >9360</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow2_col3\" class=\"data row2 col3\" >22500</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow2_col4\" class=\"data row2 col4\" >45777</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow2_col5\" class=\"data row2 col5\" >192</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow2_col6\" class=\"data row2 col6\" >5.67%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow3_col0\" class=\"data row3 col0\" >合资</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow3_col1\" class=\"data row3 col1\" >24030</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow3_col2\" class=\"data row3 col2\" >5250</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow3_col3\" class=\"data row3 col3\" >22500</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow3_col4\" class=\"data row3 col4\" >50000</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow3_col5\" class=\"data row3 col5\" >319</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow3_col6\" class=\"data row3 col6\" >9.41%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow4_col0\" class=\"data row4 col0\" >民营公司</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow4_col1\" class=\"data row4 col1\" >22457</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow4_col2\" class=\"data row4 col2\" >7000</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow4_col3\" class=\"data row4 col3\" >20000</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow4_col4\" class=\"data row4 col4\" >50000</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow4_col5\" class=\"data row4 col5\" >2142</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow4_col6\" class=\"data row4 col6\" >63.20%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow5_col0\" class=\"data row5 col0\" >国企</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow5_col1\" class=\"data row5 col1\" >20323</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow5_col2\" class=\"data row5 col2\" >7705</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow5_col3\" class=\"data row5 col3\" >20000</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow5_col4\" class=\"data row5 col4\" >37500</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow5_col5\" class=\"data row5 col5\" >410</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow5_col6\" class=\"data row5 col6\" >12.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow6_col0\" class=\"data row6 col0\" >事业单位</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow6_col1\" class=\"data row6 col1\" >18659</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow6_col2\" class=\"data row6 col2\" >8400</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow6_col3\" class=\"data row6 col3\" >17500</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow6_col4\" class=\"data row6 col4\" >35000</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow6_col5\" class=\"data row6 col5\" >68</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow6_col6\" class=\"data row6 col6\" >2.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow7_col0\" class=\"data row7 col0\" >非营利组织</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow7_col1\" class=\"data row7 col1\" >14233</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow7_col2\" class=\"data row7 col2\" >9000</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow7_col3\" class=\"data row7 col3\" >10875</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow7_col4\" class=\"data row7 col4\" >40000</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow7_col5\" class=\"data row7 col5\" >15</td>\n",
       "                        <td id=\"T_0211e5f6_74fa_11ea_9f10_701ce71031efrow7_col6\" class=\"data row7 col6\" >0.44%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15717d67948>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_type=get_sub_stats_by_col(data,'company_type')\n",
    "apply_style(data_company_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_0312e002_74fa_11ea_9266_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >company_size</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow0_col0\" class=\"data row0 col0\" >10000+</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow0_col1\" class=\"data row0 col1\" >33643</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow0_col2\" class=\"data row0 col2\" >5250</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow0_col3\" class=\"data row0 col3\" >35000</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow0_col4\" class=\"data row0 col4\" >55805</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow0_col5\" class=\"data row0 col5\" >390</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow0_col6\" class=\"data row0 col6\" >11.51%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow1_col0\" class=\"data row1 col0\" >500-1000</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow1_col1\" class=\"data row1 col1\" >24587</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow1_col2\" class=\"data row1 col2\" >12033</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow1_col3\" class=\"data row1 col3\" >22500</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow1_col4\" class=\"data row1 col4\" >46389</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow1_col5\" class=\"data row1 col5\" >364</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow1_col6\" class=\"data row1 col6\" >10.74%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow2_col0\" class=\"data row2 col0\" >150-500</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow2_col1\" class=\"data row2 col1\" >22993</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow2_col2\" class=\"data row2 col2\" >7000</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow2_col3\" class=\"data row2 col3\" >20690</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow2_col4\" class=\"data row2 col4\" >57500</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow2_col5\" class=\"data row2 col5\" >735</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow2_col6\" class=\"data row2 col6\" >21.69%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow3_col0\" class=\"data row3 col0\" >5000-10000</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow3_col1\" class=\"data row3 col1\" >22733</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow3_col2\" class=\"data row3 col2\" >9807</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow3_col3\" class=\"data row3 col3\" >21627</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow3_col4\" class=\"data row3 col4\" >36021</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow3_col5\" class=\"data row3 col5\" >166</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow3_col6\" class=\"data row3 col6\" >4.90%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow4_col0\" class=\"data row4 col0\" >1000-5000</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow4_col1\" class=\"data row4 col1\" >20354</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow4_col2\" class=\"data row4 col2\" >7500</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow4_col3\" class=\"data row4 col3\" >20000</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow4_col4\" class=\"data row4 col4\" >44750</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow4_col5\" class=\"data row4 col5\" >565</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow4_col6\" class=\"data row4 col6\" >16.67%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow5_col0\" class=\"data row5 col0\" >50-150</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow5_col1\" class=\"data row5 col1\" >19695</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow5_col2\" class=\"data row5 col2\" >6890</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow5_col3\" class=\"data row5 col3\" >17500</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow5_col4\" class=\"data row5 col4\" >50000</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow5_col5\" class=\"data row5 col5\" >718</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow5_col6\" class=\"data row5 col6\" >21.19%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow6_col0\" class=\"data row6 col0\" >50-</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow6_col1\" class=\"data row6 col1\" >19611</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow6_col2\" class=\"data row6 col2\" >5312</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow6_col3\" class=\"data row6 col3\" >15000</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow6_col4\" class=\"data row6 col4\" >58604</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow6_col5\" class=\"data row6 col5\" >367</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow6_col6\" class=\"data row6 col6\" >10.83%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow7_col0\" class=\"data row7 col0\" ></td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow7_col1\" class=\"data row7 col1\" >18601</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow7_col2\" class=\"data row7 col2\" >8200</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow7_col3\" class=\"data row7 col3\" >17500</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow7_col4\" class=\"data row7 col4\" >41667</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow7_col5\" class=\"data row7 col5\" >84</td>\n",
       "                        <td id=\"T_0312e002_74fa_11ea_9266_701ce71031efrow7_col6\" class=\"data row7 col6\" >2.48%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15717f0d208>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_size=get_sub_stats_by_col(data,'company_size')\n",
    "apply_style(data_company_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "公司越大，工资越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >industry</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow0_col0\" class=\"data row0 col0\" ></td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow0_col1\" class=\"data row0 col1\" >27976</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow0_col2\" class=\"data row0 col2\" >12562</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow0_col3\" class=\"data row0 col3\" >30667</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow0_col4\" class=\"data row0 col4\" >35000</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow0_col5\" class=\"data row0 col5\" >21</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.62%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow1_col0\" class=\"data row1 col0\" >gov</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow1_col1\" class=\"data row1 col1\" >24991</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow1_col2\" class=\"data row1 col2\" >9000</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow1_col3\" class=\"data row1 col3\" >23286</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow1_col4\" class=\"data row1 col4\" >58333</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow1_col5\" class=\"data row1 col5\" >37</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow1_col6\" class=\"data row1 col6\" >1.09%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow2_col0\" class=\"data row2 col0\" >computer</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow2_col1\" class=\"data row2 col1\" >23600</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow2_col2\" class=\"data row2 col2\" >7000</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow2_col3\" class=\"data row2 col3\" >22500</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow2_col4\" class=\"data row2 col4\" >49604</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow2_col5\" class=\"data row2 col5\" >2519</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow2_col6\" class=\"data row2 col6\" >74.33%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow3_col0\" class=\"data row3 col0\" >finance</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow3_col1\" class=\"data row3 col1\" >22959</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow3_col2\" class=\"data row3 col2\" >7000</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow3_col3\" class=\"data row3 col3\" >20436</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow3_col4\" class=\"data row3 col4\" >68108</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow3_col5\" class=\"data row3 col5\" >162</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow3_col6\" class=\"data row3 col6\" >4.78%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow4_col0\" class=\"data row4 col0\" >edu</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow4_col1\" class=\"data row4 col1\" >22280</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow4_col2\" class=\"data row4 col2\" >6497</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow4_col3\" class=\"data row4 col3\" >17500</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow4_col4\" class=\"data row4 col4\" >62588</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow4_col5\" class=\"data row4 col5\" >293</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow4_col6\" class=\"data row4 col6\" >8.65%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow5_col0\" class=\"data row5 col0\" >logistic</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow5_col1\" class=\"data row5 col1\" >18123</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow5_col2\" class=\"data row5 col2\" >8000</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow5_col3\" class=\"data row5 col3\" >16666</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow5_col4\" class=\"data row5 col4\" >27994</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow5_col5\" class=\"data row5 col5\" >23</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow5_col6\" class=\"data row5 col6\" >0.68%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow6_col0\" class=\"data row6 col0\" >medical</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow6_col1\" class=\"data row6 col1\" >17975</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow6_col2\" class=\"data row6 col2\" >7600</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow6_col3\" class=\"data row6 col3\" >16805</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow6_col4\" class=\"data row6 col4\" >38500</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow6_col5\" class=\"data row6 col5\" >92</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow6_col6\" class=\"data row6 col6\" >2.71%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow7_col0\" class=\"data row7 col0\" >energy</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow7_col1\" class=\"data row7 col1\" >17672</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow7_col2\" class=\"data row7 col2\" >1000</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow7_col3\" class=\"data row7 col3\" >18333</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow7_col4\" class=\"data row7 col4\" >37500</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow7_col5\" class=\"data row7 col5\" >39</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow7_col6\" class=\"data row7 col6\" >1.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow8_col0\" class=\"data row8 col0\" >ads</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow8_col1\" class=\"data row8 col1\" >17061</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow8_col2\" class=\"data row8 col2\" >7000</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow8_col3\" class=\"data row8 col3\" >17045</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow8_col4\" class=\"data row8 col4\" >40125</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow8_col5\" class=\"data row8 col5\" >30</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow8_col6\" class=\"data row8 col6\" >0.89%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow9_col0\" class=\"data row9 col0\" >trade</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow9_col1\" class=\"data row9 col1\" >16994</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow9_col2\" class=\"data row9 col2\" >6289</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow9_col3\" class=\"data row9 col3\" >13792</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow9_col4\" class=\"data row9 col4\" >50021</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow9_col5\" class=\"data row9 col5\" >121</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow9_col6\" class=\"data row9 col6\" >3.57%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow10_col0\" class=\"data row10 col0\" >service</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow10_col1\" class=\"data row10 col1\" >13147</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow10_col2\" class=\"data row10 col2\" >12011</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow10_col3\" class=\"data row10 col3\" >12656</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow10_col4\" class=\"data row10 col4\" >25625</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow10_col5\" class=\"data row10 col5\" >34</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow10_col6\" class=\"data row10 col6\" >1.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow11_col0\" class=\"data row11 col0\" >realestate</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow11_col1\" class=\"data row11 col1\" >9028</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow11_col2\" class=\"data row11 col2\" >7000</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow11_col3\" class=\"data row11 col3\" >7650</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow11_col4\" class=\"data row11 col4\" >25000</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow11_col5\" class=\"data row11 col5\" >18</td>\n",
       "                        <td id=\"T_03b5ad64_74fa_11ea_8c5e_701ce71031efrow11_col6\" class=\"data row11 col6\" >0.53%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15717f99488>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_industry=get_sub_stats_by_col(data,'industry')\n",
    "apply_style(data_industry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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   "pygments_lexer": "ipython3",
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 },
 "nbformat": 4,
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